131 Statsmodels Jobs

About GreedyGame GreedyGame is a leading ad-tech company that's been driving app growth and monetization. We’ve built a sustainable and profitable business for over 7 years without chasing vanity metrics and successfully completed our 0→1 journey, we’re now scaling rapidly (1→10 phase) As a Google Publishing Partner, we serve 100M+ daily ad impressions and support 5,000+ apps and games worldwide—delivering up to 75% revenue uplift Trusted by brands like Amazon, Dream11, MPL, Sharechat, Treebo, Mobikwik, and 1000+ publishers across 20+ countries. We are looking for a Data Scientist who can turn this data into foresight building models that predict campaign outcomes, forecast publisher revenue, and power decision-making at scale. Responsibilities- Revenue Forecasting: Build predictive models to estimate publisher revenue streams, fill rates, and eCPM across different ad networks and geographies. User Behavior Prediction: Analyze in-app user journeys and predict churn, retention, and engagement to help publishers personalize experiences. Ad Performance Modeling: Develop CTR and conversion rate prediction models to optimize bidding strategies and ad placements. Inventory Forecasting: Forecast ad inventory availability and demand to help sales and publisher teams plan better. Campaign Optimization: Work with account managers and product teams to provide real-time insights on campaign pacing, under-delivery risks, and performance anomalies. Experimentation: Design and run experiments (A/B/n tests) to evaluate new ad formats, placements, and targeting algorithms. Data Products: Partner with engineering to embed forecasting models into GreedyGame’s platform, enabling self-serve insights for publishers and advertisers. Requirements - 2–4 years of hands-on experience in predictive analytics, time series forecasting, or applied ML. Strong proficiency in Python (Pandas, NumPy, Scikit-learn, Statsmodels) and SQL. Familiarity with MLOps practices (model deployment, monitoring, retraining). Solid understanding of statistical inference, experiment design, and causal analysis. Knowledge of real-time prediction pipelines (Kafka, Spark) Nice to Have Exposure to adtech, gaming analytics, or digital marketing intelligence. Familiarity with retrieval-augmented generation (RAG) or fine-tuning for domain-specific insights. Experience with cloud AI/ML services (GCP Vertex AI, AWS Sagemaker, Azure ML). Exposure to GenAI and LLMs for applied use cases (e.g., automated report generation, text summarization of campaign insights, anomaly explanation, or predictive storytelling). 🎁 Why GreedyGame? Opportunity to work on products used by millions – see: Pubscale Ownership from Day 1 – shape architecture, strategy, and key decisions Learning stipend for books, courses, and conferences (we love curious minds!) internal blogs Flexible hybrid work setup – split time between our Bangalore HQ and remote work Comprehensive benefits – health insurance, PTO, parental leave, and more A high-growth, inclusive, and engineering-led culture that supports experimentation and impact Skills: forecasting,predictive analytics,suggestions,revenue,ml,prediction,app,sql

Job ID: 274686 Technology - Backend Hybrid Bangalore, Karnataka, India 2 - 5 years About GreedyGame GreedyGame is a leading ad-tech company that's been driving app growth and monetization. We’ve built a sustainable and profitable business for over 7 years without chasing vanity metrics and successfully completed our 0 1 journey, we’re now scaling rapidly (1 10 phase) As a Google Publishing Partner , we serve 100M+ daily ad impressions and support 5,000+ apps and games worldwide—delivering up to 75% revenue uplift Trusted by brands like Amazon, Dream11, MPL, Sharechat, Treebo, Mobikwik , and 1000+ publishers across 20+ countries. We are looking for a Data Scientist who can turn this data into foresight building models that predict campaign outcomes, forecast publisher revenue, and power decision-making at scale. Responsibilities- Revenue Forecasting : Build predictive models to estimate publisher revenue streams, fill rates, and eCPM across different ad networks and geographies. User Behavior Prediction : Analyze in-app user journeys and predict churn, retention, and engagement to help publishers personalize experiences. Ad Performance Modeling : Develop CTR and conversion rate prediction models to optimize bidding strategies and ad placements. Inventory Forecasting : Forecast ad inventory availability and demand to help sales and publisher teams plan better. Campaign Optimization : Work with account managers and product teams to provide real-time insights on campaign pacing, under-delivery risks, and performance anomalies. Experimentation : Design and run experiments (A/B/n tests) to evaluate new ad formats, placements, and targeting algorithms. Data Products : Partner with engineering to embed forecasting models into GreedyGame’s platform, enabling self-serve insights for publishers and advertisers. Requirements - 2–4 years of hands-on experience in predictive analytics , time series forecasting, or applied ML. Strong proficiency in Python (Pandas, NumPy, Scikit-learn, Statsmodels) and SQL. Familiarity with MLOps practices (model deployment, monitoring, retraining). Solid understanding of statistical inference, experiment design, and causal analysis. Knowledge of real-time prediction pipelines (Kafka, Spark) Nice to Have Exposure to adtech, gaming analytics, or digital marketing intelligence. Familiarity with retrieval-augmented generation (RAG) or fine-tuning for domain-specific insights. Experience with cloud AI/ML services (GCP Vertex AI, AWS Sagemaker, Azure ML). Exposure to GenAI and LLMs for applied use cases (e.g., automated report generation, text summarization of campaign insights, anomaly explanation, or predictive storytelling). Why GreedyGame? Opportunity to work on products used by millions – see: Pubscale Ownership from Day 1 – shape architecture, strategy, and key decisions Learning stipend for books, courses, and conferences (we love curious minds!) internal blogs Flexible hybrid work setup – split time between our Bangalore HQ and remote work Comprehensive benefits – health insurance, PTO, parental leave, and more A high-growth, inclusive, and engineering-led culture that supports experimentation and impact Workplace Type Hybrid Employment Type Full-Time Experience Level Mid-Senior-Level Work Experience (years) 2 - 5 years Skills Forecasting Predictive Analytics Suggestions Revenue Ml Prediction App Sql

As a Data Scientist at Texas Instruments, you will be a key player in the Demand Analytics team, focusing on shaping and executing demand planning and inventory buffer strategies. Working alongside a team of technical professionals, including application developers, system architects, data scientists, and data engineers, you will be responsible for solving complex business problems through innovative solutions that drive tangible business value. Your role will involve portfolio management for demand forecasting algorithms, generation of inventory buffer targets, segmentation of products, simulation/validation frameworks, and ensuring security and interoperability between capabilities. Key Responsibilities: - Engage strategically with stakeholder groups to align with TI's business strategy and goals - Communicate complex technical concepts effectively to influence final business outcomes - Collaborate with cross-functional teams to identify and prioritize actionable insights - Build scalable and modular technology stacks using modern technologies - Conduct simulations with various models to determine the best fit of algorithms - Research, experiment, and implement new approaches and models in line with business strategy - Lead data acquisition and engineering efforts - Develop and apply machine learning, AI, and data engineering frameworks - Write and debug code for complex development projects - Evaluate and determine the best modeling techniques for different scenarios Qualifications: Minimum requirements: - MS or PhD in a quantitative field or equivalent practical experience - 8+ years of professional experience in data science or related roles - 5+ years of hands-on experience developing and deploying time series forecasting models - Deep understanding of supply chain concepts like demand forecasting and inventory management - Proficiency in Python and core data science libraries - Experience taking machine learning models from prototype to production Preferred qualifications: - Experience with MLOps tools and platforms - Practical experience with cloud data science platforms - Familiarity with advanced forecasting techniques and NLP - Strong SQL skills and experience with large-scale data warehousing solutions About Texas Instruments: Texas Instruments is a global semiconductor company that designs, manufactures, and sells analog and embedded processing chips for various markets. At TI, we are passionate about creating a better world by making electronics more affordable through semiconductors. Our commitment to innovation drives us to build technology that is reliable, affordable, and energy-efficient, enabling semiconductors to be used in electronics everywhere. Join TI to engineer your future and collaborate with some of the brightest minds to shape the future of electronics. Embrace diversity and inclusion at TI, where every voice is valued and contributes to our strength as an organization. If you are ready to make an impact in the field of data science, apply to join our team at Texas Instruments.,

Description: As a Data Scientist at Encardio, you will analyze complex time-series data from devices such as accelerometers, strain gauges, and tilt meters. Your responsibilities will span data preprocessing, feature engineering, machine learning model development, and integration with real-time systems. You'll collaborate closely with engineers and domain experts to translate physical behaviours into actionable insights. This role is ideal for someone with strong statistical skills, experience in time-series modeling, and a desire to understand the real-world impact of their models in civil and industrial monitoring. Responsibilities Sensor Data Understanding & Preprocessing Clean, denoise, and preprocess high-frequency time-series data from edge devices. Handle missing, corrupted, or delayed telemetry from IoT sources. Develop domain knowledge of physical sensors and their behaviour (e.g., vibration patterns, strain profiles). Exploratory & Statistical Analysis Perform statistical and exploratory data analysis (EDA) on structured/unstructured sensor data. Identify anomalies, patterns, and correlations in multi-sensor environments. Feature Engineering Generate meaningful time-domain and frequency-domain features (e.g., FFT, wavelets). Implement scalable feature extraction pipelines. Model Development Build and validate ML models for: Anomaly detection (e.g., vibration spikes) Event classification (e.g., tilt angle breaches) Predictive maintenance (e.g., time-to-failure) Leverage traditional ML and deep learning and LLMs Deployment & Integration Work with Data Engineers to integrate models into real-time data pipelines and edge/cloud platforms. Package and containerize models (e.g., with Docker) for scalable deployment. Monitoring & Feedback Track model performance post-deployment and retrain/update as needed. Design feedback loops using human-in-the-loop or rule-based corrections. Collaboration & Communication Collaborate with hardware, firmware, and data engineering teams. Translate physical phenomena into data problems and insights. Document approaches, models, and assumptions for reproducibility. 🎯 Key Deliverables Reusable preprocessing and feature extraction modules for sensor data. Accurate and explainable ML models for anomaly/event detection. Model deployment artifacts (Docker images, APIs) for cloud or edge execution. Jupyter notebooks and dashboards (streamlit) for diagnostics, visualization, and insight generation. Model monitoring reports and performance metrics with retraining pipelines. Domain-specific data dictionaries and technical knowledge bases. Contribution to internal documentation and research discussions. Build deep understanding and documentation of sensor behavior and characteristics. 🔧 Technologies Languages & Libraries Python (NumPy, Pandas, SciPy, Scikit-learn, PyTorch/TensorFlow) Bash (for data ops & batch jobs) Signal Processing & Feature Extraction FFT, DWT, STFT (via SciPy, Librosa, tsfresh) Time-series modeling (sktime, statsmodels, Prophet) Machine Learning & Deep Learning Scikit-learn (traditional ML) PyTorch / TensorFlow / Keras (deep learning) XGBoost / LightGBM (tabular modeling) Data Analysis & Visualization Jupyter, Matplotlib, Seaborn, Plotly, Grafana (for dashboards) Model Deployment Docker (for containerizing ML models) FastAPI / Flask (for ML inference APIs) GitHub Actions (CI/CD for models) ONNX / TorchScript (for lightweight deployment) Data Engineering Integration Kafka (real-time data ingestion) S3 (model/data storage) Trino / Athena (querying raw and processed data) Argo Workflows / Airflow (model training pipelines) Monitoring & Observability Prometheus / Grafana (model & system monitoring)

Company Description Blend is building a scalable Media Mix Optimization (MMO) solution designed to help clients maximize the impact of their marketing investments. We are seeking a Data Scientist with strong expertise in media mix modeling, statistical modeling, and interactive application development to join our advanced analytics team. This role goes beyond model building you will design, implement, and productionize end-to-end solutions that integrate statistical rigor with business impact. The ideal candidate will have deep knowledge of marketing analytics, advanced Python skills, and hands-on experience with Streamlit or similar frameworks for interactive data applications. You will be central in creating robust pipelines, experimentation frameworks, and client-facing tools that directly inform media allocation decisions. Job Description Our MMO platform is an in-house initiative designed to empower clients with data-driven decision-making in marketing strategy. By applying Bayesian and frequentist approaches to media mix modeling , we are able to quantify channel-level ROI, measure incrementality, and simulate outcomes under varying spend scenarios. Key Components Of The Project Include Data Integration: Combining client first-party, third-party, and campaign-level data across digital, offline, and emerging channels into a unified modeling framework. Model Development: Building and validating media mix models (MMM) using advanced statistical and machine learning techniques such as hierarchical Bayesian regression, regularized regression (Ridge/Lasso), and time-series modeling. Scenario Simulation: Enabling stakeholders to forecast outcomes under different budget allocations through simulation and optimization algorithms. Deployment & Visualization: Using Streamlit to build interactive, client-facing dashboards for model exploration, scenario planning, and actionable recommendation delivery. Scalability: Engineering the system to support multiple clients across industries with varying data volumes, refresh cycles, and modeling complexities. Responsibilities Develop, validate, and maintain media mix models to evaluate cross-channel marketing effectiveness and return on investment. Engineer and optimize end-to-end data pipelines for ingesting, cleaning, and structuring large, heterogeneous datasets from multiple marketing and business sources. Design, build, and deploy Streamlit-based interactive dashboards and applications for scenario testing, optimization, and reporting. Conduct exploratory data analysis (EDA) and advanced feature engineering to identify drivers of performance. Apply Bayesian methods, regularization, and time-series analysis to improve model accuracy, stability, and interpretability. Implement optimization and scenario-planning algorithms to recommend budget allocation strategies that maximize business outcomes. Collaborate closely with product, engineering, and client teams to align technical solutions with business objectives. Present insights and recommendations to senior stakeholders in both technical and non- technical language. Stay current with emerging tools, techniques, and best practices in media mix modeling, causal inference, and marketing science. Qualifications Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Applied Mathematics, or related field. Proven hands-on experience in media mix modeling, marketing analytics, or econometrics. Strong proficiency in Python and key data science libraries (pandas, NumPy, scikit-learn, statsmodels, PyMC or similar Bayesian frameworks). Experience with Streamlit or equivalent frameworks (Dash, Shiny) for building data- driven applications. Proficiency in SQL for querying, joining, and optimizing large-scale datasets. Solid foundation in statistical modeling, regression techniques, and machine learning. Strong problem-solving skills with the ability to structure ambiguous business problems into data-driven solutions. Excellent verbal and written communication skills to translate technical outputs into business decisions. Preferred Qualifications Experience with Bayesian hierarchical models, time-series decomposition, and marketing attribution approaches. Familiarity with cloud-based platforms (AWS, GCP, Azure) for data processing, model training, and deployment. Experience with data visualization tools beyond Streamlit (Tableau, Power BI, D3.js, Plotly). Exposure to big data ecosystems (Spark, Hadoop) for large-scale data processing. Knowledge of causal inference techniques (propensity scoring, uplift modeling, geo- experiments).

About Us Jar is India’s leading Daily Saving app that helps people build strong saving habits—one small step at a time. Our goal is to make saving simple, rewarding, and truly life-changing. Founded in 2021 by Misbah Ashraf and Nishchay AG, Jar is a Bengaluru-based startup with one simple belief: saving a little every day in 24K Digital Gold can truly transform your future. Today, 20 million+ Indians trust Jar as their saving partner. With flexible saving options—Daily, Weekly, Monthly, and Instant Saving—we have made it easy for everyone to save in their own way and withdraw anytime. We are one of the leaders in UPI autopay transactions, crossing more than 1 million transactions per day. In 2023, we expanded our vision with Nek, our jewelry brand crafted to bring together luxury and affordability, it has since surpassed ₹100 crore in revenue. We have a big dream of bringing “Har Ghar Sona”. Small, consistent savings are just the start. We’re here to walk alongside our users, helping Indians secure their financial future every step of the way. Backed by Tiger Global Management, Arkam Ventures, and WEH Ventures, among others, we have raised $50 million+ in funding. In January 2025, we hit a huge milestone of becoming profitable. Now, we’re charging ahead, focused on sustainable growth and scaling impact. And this is just the beginning! What’s the role? As a Data Analyst - 1, you will be responsible for analyzing data to generate insights, building dashboards, and supporting decision-making across acquisition, retention and product performance. You will work closely with senior analysts, product managers, and business stakeholders to ensure accurate tracking, reporting, and data-driven recommendations. What will be your responsibilities? Perform data analysis and reporting on customer acquisition, retention, churn, and transactions. Build, maintain, and automate dashboards and reports for business stakeholders. Conduct deep dives into structured and unstructured data to identify trends, anomalies, and actionable insights. Support Root Cause Analysis (RCA) for business metric changes and provide recommendations. Partner with product and business teams to define, track, and measure key performance indicators (KPIs). Assist in A/B experiments, feature evaluations, and campaign analysis. Ensure data accuracy, consistency, and completeness across reporting pipelines. Collaborate with engineers and analysts to improve data quality and infrastructure. What’s required from you? Bachelor’s degree in Engineering, Statistics, Mathematics, Economics, Computer Science, or related field. 1–3 years of experience in a data analytics role, preferably in fintech, e-commerce, or consumer tech. Strong proficiency in Python & MongoDb for data analysis, including Pandas, NumPy, Scikit-learn, Matplotlib/Seaborn, Dask, Statsmodels, Re(regular expressions for text cleaning), textblob (sentiment analysis & text processing) & Automations. Object oriented programming is a plus. SQL: Expertise in writing complex sql (postgres) queries for data extraction, transformation, and reporting. Process, clean, and wrangle large datasets using Python to ensure data quality and readiness for analysis. Strong understanding of Excel/Google Sheets for quick ad-hoc analysis. Experience working with large, structured/unstructured datasets. Visualization Tools: Data Exploration and Visualisation with tools like Amplitude, Clevertap, MS Excel, Tableau, Power BI, etc. Familiarity with A/B testing frameworks and statistical concepts. Strong analytical thinking, problem-solving skills, and attention to detail. Good communication skills to explain insights to non-technical stakeholders. What makes us different? We’re not just building a product—we’re shaping the future of savings in India. We seek people who bring passion, energy, and fresh ideas to help us make that happen. Experience matters, but we are a potential first organisation. We move fast, learn from our mistakes, and take bold risks to solve problems that haven’t been attempted before. If you’re excited about working in an environment where people inspire and truly support each other, you’ve found the right fit. What do we stand for? The five values that we live by : Passion: At Jar, we strive to create an environment where people love what they do, are motivated and equipped to do their best work. Equality: We bring diverse skills, ideas, and experiences to the table, supporting and challenging each other across teams to create something bigger than ourselves. Growth: When our people grow, Jar grows. We create opportunities for learning, development, and meaningful impact. Accountability: The core of our work ethic is taking ownership of our work, showing initiative, and having the freedom to ask questions. Consistency: We believe in doing the right things consistently. Big change doesn’t happen overnight, it’s built one step at a time. Join us and let’s build something amazing together! What employee benefits do we have? Glad you asked! Among other things, we have Medical Insurance for employees and their families ESOPs allocation Pluxee meal card Swish club card for exclusive employee discounts Advance salary plans Relocation assistance L&D programmes

Job Description Job Title: Engineer, Manufacturing II (Integration) Reports To: Engineering Manager Integration Description Identifying and implementing opportunities for efficient process engineering utilizing data, modeling and applying Industry4.0/ Advanced analytics concepts for improving Yield, Wattage and process control Education And Experience Bachelor’s Degree in engineering, physics or chemistry with 5+ years of experience in Semiconductor Manufacturing setting Master’s Degree with at-least three year of experience in Semiconductor Manufacturing settings, or PhD in an engineering or similar technical discipline with at-least 1year of experience required. Technical work experience in combination with a degree is preferred. Required Skills/Competencies General Skills: Strong track record of effective problem-solving, and root-cause analysis working with cross functional technical teams Understanding of manufacturing engineering systems (FMEA, SPC) and structured problem solving methods (such as DMAIC) Demonstrated proficiency in Statistical Data Analysis, DOE and experience using any statistical software (Like Minitab, JMP, SAS, Matlab etc Experienced with standard data science and machine learning packages Have good understanding of the machine learning model stack (regression, classification, neural networks, time series) and packages (Scikit learn, Xgboost, Keras, Pytorch, Tensorflow, statsmodels) Background education in physics, materials science preferred Background in solar manufacturing is a plus Excellent oral and written communications skills Experience in Semiconductor Device Physics is a plus Desired Skills Ability to work with minimal supervision Experience in high volume ramp and/or new tech transfer Lean Manufacturing knowledge Essential Responsibilities General responsibilities: Ensure adherence to all safety/environmental procedures Ability to lead discussion with cross-functional departments and satellite sites to ensure timely communication and effective problem solving Design statistical experiments and Analyze data using sound statistical methodology to drive improvements and recommendations to operations of process engineering Provide engineering support for high volume manufacturing across multiple engineering departments in a global organization, working with international teams in cross functional environment Develop and provide training to engineering technicians, and production associates Analyze data and trends, correlate to metrics and identify data driven solution for detection and alert Implement production solution for efficient use of metric monitoring and visualization to identify top issues for the day Implement advanced data models for wattage prediction and label for solar panel utilizing process inputs and outputs Predictive analysis for optimizing throughput, yield and improve process control Utilize Industry4.0 elements to improve process and productivity Develop proof of concepts and scale them up to production levels locally and cross plant Reporting Relationships This position could have direct reports Physical Requirements Will sit, stand or walk short distances for up to 12 hours per day. Will climb stairs on an occasional basis. Will lift up to 51 pounds on an occasional basis, according to work instruction. Will lift up to 37 pounds on a frequent basis Will push or pull up to 27 pounds of force on an occasional basis. Will push or pull up to 10 pounds of force on a frequent basis. Required to use hands to grasp, lift, handle, carry or feel objects repetitively on a frequent basis. 20/40 vision in each eye (with or without correction) and the ability to distinguish between red, yellow and green is required. May reach above shoulder heights and below the waist on a frequent basis. May stoop, kneel, or bend, on an occasional basis. Must be able to comply with all safety standards and procedures. Ability to wear personal protective equipment is required (including but not limited to; steel toed shoes, cut resistant gloves, jackets, aprons &/or arm guards, safety glasses, hearing protection). All associates working on the production floor may be required to wear a respirator at any given time and thus, the ability to wear a respirator is a condition of employment and continued employment (requires little or no facial hair). Potential candidates will meet the education and experience requirements provided on the above job description and excel in completing the listed responsibilities for this role. All candidates receiving an offer of employment must successfully complete a background check band any other tests that may be required. Equal Opportunity Employer Statement: First Solar is an Equal Opportunity Employer that values and respects the importance of a diverse and inclusive workforce. It is the policy of the company to recruit, hire, train and promote persons in all job titles without regard to race, color, religion, sex, age, national origin, veteran status, disability, sexual orientation, or gender identity. We recognize that diversity and inclusion is a driving force in the success of our company.

Job Description Control Status: Controlled Document Title: Engineer, Manufacturing I (Integration) Roles & Responsibilities Job Title: Engineer, Manufacturing I (Integration) Reports To: Engineering Manager Integration Description Identifying and implementing opportunities for efficient process engineering utilizing data, modeling and applying Industry4.0/ Advanced analytics concepts for improving Yield, Wattage and process control Education And Experience Bachelor’s Degree in engineering, physics or chemistry with 3+ years of experience in Semiconductor Manufacturing setting Master’s Degree with at-least two year of experience in Semiconductor Manufacturing settings, or PhD in an engineering or similar technical discipline with at-least 1year of experience required. Technical work experience in combination with a degree is preferred. Required Skills/Competencies General Skills: Strong track record of effective problem-solving, and root-cause analysis working with cross functional technical teams Understanding of manufacturing engineering systems (FMEA, SPC) and structured problem solving methods (such as DMAIC) Demonstrated proficiency in Statistical Data Analysis, DOE and experience using any statistical software (Like Minitab, JMP, SAS, Matlab etc Experienced with standard data science and machine learning packages Have good understanding of the machine learning model stack (regression, classification, neural networks, time series) and packages (Scikit learn, Xgboost, Keras, Pytorch, Tensorflow, statsmodels) Background education in physics, materials science preferred Background in solar manufacturing is a plus Excellent oral and written communications skills Experience in Semiconductor Device Physics is a plus Desired Skills Ability to work with minimal supervision Experience in high volume ramp and/or new tech transfer Lean Manufacturing knowledge Essential Responsibilities General responsibilities: Ensure adherence to all safety/environmental procedures Ability to lead discussion with cross-functional departments and satellite sites to ensure timely communication and effective problem solving Design statistical experiments and Analyze data using sound statistical methodology to drive improvements and recommendations to operations of process engineering Provide engineering support for high volume manufacturing across multiple engineering departments in a global organization, working with international teams in cross functional environment Develop and provide training to engineering technicians, and production associates Analyze data and trends, correlate to metrics and identify data driven solution for detection and alert Implement production solution for efficient use of metric monitoring and visualization to identify top issues for the day Implement advanced data models for wattage prediction and label for solar panel utilizing process inputs and outputs Predictive analysis for optimizing throughput, yield and improve process control Utilize Industry4.0 elements to improve process and productivity Develop proof of concepts and scale them up to production levels locally and cross plant Physical Requirements Will sit, stand or walk short distances for up to 12 hours per day. Will climb stairs on an occasional basis. Will lift up to 51 pounds on an occasional basis, according to work instruction. Will lift up to 37 pounds on a frequent basis Will push or pull up to 27 pounds of force on an occasional basis. Will push or pull up to 10 pounds of force on a frequent basis. Required to use hands to grasp, lift, handle, carry or feel objects repetitively on a frequent basis. 20/40 vision in each eye (with or without correction) and the ability to distinguish between red, yellow and green is required. May reach above shoulder heights and below the waist on a frequent basis. May stoop, kneel, or bend, on an occasional basis. Must be able to comply with all safety standards and procedures. Ability to wear personal protective equipment is required (including but not limited to; steel toed shoes, cut resistant gloves, jackets, aprons &/or arm guards, safety glasses, hearing protection). All associates working on the production floor may be required to wear a respirator at any given time and thus, the ability to wear a respirator is a condition of employment and continued employment (requires little or no facial hair). Potential candidates will meet the education and experience requirements provided on the above job description and excel in completing the listed responsibilities for this role. All candidates receiving an offer of employment must successfully complete a background check band any other tests that may be required. Equal Opportunity Employer Statement: First Solar is an Equal Opportunity Employer that values and respects the importance of a diverse and inclusive workforce. It is the policy of the company to recruit, hire, train and promote persons in all job titles without regard to race, color, religion, sex, age, national origin, veteran status, disability, sexual orientation, or gender identity. We recognize that diversity and inclusion is a driving force in the success of our company.

Job ID: 272985 Technology - Backend Hybrid Bangalore, Karnataka, India 2 - 5 years About GreedyGame GreedyGame is a leading ad-tech company that's been driving app growth and monetization. We’ve built a sustainable and profitable business for over 7 years without chasing vanity metrics and successfully completed our 0 1 journey, we’re now scaling rapidly (1 10 phase) As a Google Publishing Partner , we serve 100M+ daily ad impressions and support 5,000+ apps and games worldwide—delivering up to 75% revenue uplift Trusted by brands like Amazon, Dream11, MPL, Sharechat, Treebo, Mobikwik , and 1000+ publishers across 20+ countries. We are looking for a Data Scientist who can turn this data into foresight building models that predict campaign outcomes, forecast publisher revenue, and power decision-making at scale. Responsibilities- Revenue Forecasting : Build predictive models to estimate publisher revenue streams, fill rates, and eCPM across different ad networks and geographies. User Behavior Prediction : Analyze in-app user journeys and predict churn, retention, and engagement to help publishers personalize experiences. Ad Performance Modeling : Develop CTR and conversion rate prediction models to optimize bidding strategies and ad placements. Inventory Forecasting : Forecast ad inventory availability and demand to help sales and publisher teams plan better. Campaign Optimization : Work with account managers and product teams to provide real-time insights on campaign pacing, under-delivery risks, and performance anomalies. Experimentation : Design and run experiments (A/B/n tests) to evaluate new ad formats, placements, and targeting algorithms. Data Products : Partner with engineering to embed forecasting models into GreedyGame’s platform, enabling self-serve insights for publishers and advertisers. Requirements - 2–4 years of hands-on experience in predictive analytics , time series forecasting, or applied ML. Strong proficiency in Python (Pandas, NumPy, Scikit-learn, Statsmodels) and SQL. Familiarity with MLOps practices (model deployment, monitoring, retraining). Solid understanding of statistical inference, experiment design, and causal analysis. Knowledge of real-time prediction pipelines (Kafka, Spark) Nice to Have Exposure to adtech, gaming analytics, or digital marketing intelligence. Familiarity with retrieval-augmented generation (RAG) or fine-tuning for domain-specific insights. Experience with cloud AI/ML services (GCP Vertex AI, AWS Sagemaker, Azure ML). Exposure to GenAI and LLMs for applied use cases (e.g., automated report generation, text summarization of campaign insights, anomaly explanation, or predictive storytelling). Why GreedyGame? Opportunity to work on products used by millions – see: Pubscale Ownership from Day 1 – shape architecture, strategy, and key decisions Learning stipend for books, courses, and conferences (we love curious minds!) internal blogs Flexible hybrid work setup – split time between our Bangalore HQ and remote work Comprehensive benefits – health insurance, PTO, parental leave, and more A high-growth, inclusive, and engineering-led culture that supports experimentation and impact Workplace Type Hybrid Employment Type Full-Time Experience Level Associate Work Experience (years) 2 - 5 years Skills Sql Mlops Data A/B Testing Genai Forecasting Python Prediction

About GreedyGame GreedyGame is a leading ad-tech company that's been driving app growth and monetization. We’ve built a sustainable and profitable business for over 7 years without chasing vanity metrics and successfully completed our 0→1 journey, we’re now scaling rapidly (1→10 phase) As a Google Publishing Partner, we serve 100M+ daily ad impressions and support 5,000+ apps and games worldwide—delivering up to 75% revenue uplift Trusted by brands like Amazon, Dream11, MPL, Sharechat, Treebo, Mobikwik, and 1000+ publishers across 20+ countries. We are looking for a Data Scientist who can turn this data into foresight building models that predict campaign outcomes, forecast publisher revenue, and power decision-making at scale. Responsibilities- Revenue Forecasting: Build predictive models to estimate publisher revenue streams, fill rates, and eCPM across different ad networks and geographies. User Behavior Prediction: Analyze in-app user journeys and predict churn, retention, and engagement to help publishers personalize experiences. Ad Performance Modeling: Develop CTR and conversion rate prediction models to optimize bidding strategies and ad placements. Inventory Forecasting: Forecast ad inventory availability and demand to help sales and publisher teams plan better. Campaign Optimization: Work with account managers and product teams to provide real-time insights on campaign pacing, under-delivery risks, and performance anomalies. Experimentation: Design and run experiments (A/B/n tests) to evaluate new ad formats, placements, and targeting algorithms. Data Products: Partner with engineering to embed forecasting models into GreedyGame’s platform, enabling self-serve insights for publishers and advertisers. Requirements - 2–4 years of hands-on experience in predictive analytics, time series forecasting, or applied ML. Strong proficiency in Python (Pandas, NumPy, Scikit-learn, Statsmodels) and SQL. Familiarity with MLOps practices (model deployment, monitoring, retraining). Solid understanding of statistical inference, experiment design, and causal analysis. Knowledge of real-time prediction pipelines (Kafka, Spark) Nice to Have Exposure to adtech, gaming analytics, or digital marketing intelligence. Familiarity with retrieval-augmented generation (RAG) or fine-tuning for domain-specific insights. Experience with cloud AI/ML services (GCP Vertex AI, AWS Sagemaker, Azure ML). Exposure to GenAI and LLMs for applied use cases (e.g., automated report generation, text summarization of campaign insights, anomaly explanation, or predictive storytelling). 🎁 Why GreedyGame? Opportunity to work on products used by millions – see: Pubscale Ownership from Day 1 – shape architecture, strategy, and key decisions Learning stipend for books, courses, and conferences (we love curious minds!) internal blogs Flexible hybrid work setup – split time between our Bangalore HQ and remote work Comprehensive benefits – health insurance, PTO, parental leave, and more A high-growth, inclusive, and engineering-led culture that supports experimentation and impact Skills: sql,mlops,data,a/b testing,genai,forecasting,python,prediction

Job ID: 272985 Technology - Backend Hybrid Bangalore, Karnataka, India 2 - 5 years About GreedyGame GreedyGame is a leading ad-tech company that's been driving app growth and monetization. We’ve built a sustainable and profitable business for over 7 years without chasing vanity metrics and successfully completed our 0 1 journey, we’re now scaling rapidly (1 10 phase) As a Google Publishing Partner , we serve 100M+ daily ad impressions and support 5,000+ apps and games worldwide—delivering up to 75% revenue uplift Trusted by brands like Amazon, Dream11, MPL, Sharechat, Treebo, Mobikwik , and 1000+ publishers across 20+ countries. We are looking for a Data Scientist who can turn this data into foresight building models that predict campaign outcomes, forecast publisher revenue, and power decision-making at scale. Responsibilities- Revenue Forecasting : Build predictive models to estimate publisher revenue streams, fill rates, and eCPM across different ad networks and geographies. User Behavior Prediction : Analyze in-app user journeys and predict churn, retention, and engagement to help publishers personalize experiences. Ad Performance Modeling : Develop CTR and conversion rate prediction models to optimize bidding strategies and ad placements. Inventory Forecasting : Forecast ad inventory availability and demand to help sales and publisher teams plan better. Campaign Optimization : Work with account managers and product teams to provide real-time insights on campaign pacing, under-delivery risks, and performance anomalies. Experimentation : Design and run experiments (A/B/n tests) to evaluate new ad formats, placements, and targeting algorithms. Data Products : Partner with engineering to embed forecasting models into GreedyGame’s platform, enabling self-serve insights for publishers and advertisers. Requirements - 2–4 years of hands-on experience in predictive analytics , time series forecasting, or applied ML. Strong proficiency in Python (Pandas, NumPy, Scikit-learn, Statsmodels) and SQL. Familiarity with MLOps practices (model deployment, monitoring, retraining). Solid understanding of statistical inference, experiment design, and causal analysis. Knowledge of real-time prediction pipelines (Kafka, Spark) Nice to Have Exposure to adtech, gaming analytics, or digital marketing intelligence. Familiarity with retrieval-augmented generation (RAG) or fine-tuning for domain-specific insights. Experience with cloud AI/ML services (GCP Vertex AI, AWS Sagemaker, Azure ML). Exposure to GenAI and LLMs for applied use cases (e.g., automated report generation, text summarization of campaign insights, anomaly explanation, or predictive storytelling). Why GreedyGame? Opportunity to work on products used by millions – see: Pubscale Ownership from Day 1 – shape architecture, strategy, and key decisions Learning stipend for books, courses, and conferences (we love curious minds!) internal blogs Flexible hybrid work setup – split time between our Bangalore HQ and remote work Comprehensive benefits – health insurance, PTO, parental leave, and more A high-growth, inclusive, and engineering-led culture that supports experimentation and impact Workplace Type Hybrid Employment Type Full-Time Experience Level Associate Work Experience (years) 2 - 5 years Skills Sql Mlops Data A/B Testing Genai Forecasting Python Prediction

Research, backtest, and trade systematic strategies across asset classes. Build robust data pipelines, feature engineering, and ML models (tree ensembles, linear models, deep learning where justified). Use GenAI to accelerate research notes, code scaffolding, and experiment tracking; maintain strict risk limits and compliance. Strategy research with rigorous validation Backtesting with transaction costs/slippage Python (NumPy/Pandas) and ML (sklearn/torch) Risk management and monitoring GenAI for research acceleration and doc hygiene.

As a Senior Data Scientist at CDM Smith, you will play a crucial role in our Digital Engineering Solutions team, specifically within the Data Technology group. This position involves contributing to strategic initiatives in Architecture, Engineering, and Construction (AEC) by leveraging cutting-edge data technologies and analytics. You will be instrumental in providing actionable business insights and developing robust solutions that cater to the needs of AEC professionals and client outcomes. Your responsibilities will include developing and implementing predictive, prescriptive, and advanced analytics models using various statistical, optimization, and machine learning techniques to address complex AEC business challenges. You will conduct in-depth exploratory data analysis to uncover trends, relationships, and patterns in data. Additionally, you will design and implement optimization models, machine learning models, and statistical methods tailored to domain-specific issues. Collaboration with Data Engineers, AI/ML Engineers, and domain experts will be essential in framing problems and delivering actionable solutions. Your role will involve translating complex data science and optimization solutions into practical business insights through effective communication and visualization. You will also lead the evaluation and integration of advanced statistical software and tools to enhance analytical capabilities while ensuring adherence to ethical standards in AI and data science. In this role, you will need to stay updated on the latest advancements in data science, emerging technologies, best practices, tools, and software applications that could impact CDM Smith. Moreover, you will assist in developing documentation, standards, best practices, and workflows for data technology hardware/software used across the business. To excel in this position, you should possess strong expertise in statistical modeling, machine learning, optimization methods, and advanced analytics techniques. Proficiency in programming languages such as Python and R, along with strong SQL skills, will be necessary for querying and managing data effectively. Knowledge of big data platforms, distributed computing frameworks, and data visualization tools will also be beneficial. As a Senior Data Scientist at CDM Smith, you will be required to have a bachelor's degree and at least 6 years of related experience. Equivalent additional directly related experience will be considered in lieu of a degree. Your ability to effectively communicate complex technical concepts to both technical and non-technical audiences, along with your problem-solving skills and attention to detail, will be crucial for success in this role. Join us at CDM Smith and be part of a team that is at the forefront of AEC-focused Business Intelligence and data services, driving innovation and delivering impactful solutions to our clients.,

Hello Visionary! We empower our people to stay resilient and relevant in a constantly changing world. We're looking for people who are always searching for creative ways to grow and learn. People who want to make a real impact, now and in the future. Does that sound like you Then it seems like you'd make a great addition to our vibrant team. We are seeking an experienced Data Scientist with a strong foundation in Python, Machine Learning, and Cloud-based Analytics. You will play a key role in building data-driven energy optimization solutions across buildings and campuses. From time-series analysis to predictive maintenance, you'll work on impactful projects that accelerate energy efficiency and sustainability on a global scale. This position is for Chennai Location. You'll Make a Difference By: o Developing cloud-based data science solutions for energy monitoring, forecasting, and optimization . o Designing and implementing ML algorithms for anomaly detection, forecasting, and predictive simulations . o Collaborating with global software engineering teams to integrate AI/ML features into scalable cloud offerings . o Translating complex data into actionable insights through dashboards and business-friendly reports . o Investigating advanced statistical, AI, and machine learning models to solve real-world optimization problems . o Working on large-scale time-series data from IoT sensors and building automation systems . o Ensuring clean, structured, and high-quality data using modern data engineering best practices o Ability to effectively communicate in English, both written and spoken . You'll Win Us Over If You Have: o B.E. / M.Sc. / MCA / B. Tech in Computer Science / Applied Mathematics or related fields with good academic record . o 5 to 8 years of professional software development experience, with a minimum of 3 years in the analytics field (like data science, business intelligence), using professional Python 3.x & libraries o Proficiency in Python 3.x and libraries like pandas, NumPy, scikit-learn, PyTorch /TensorFlow, statsmodels . o Deep hands-on experience in machine learning, data mining, and algorithm development . o Solid understanding of time-series modeling, forecasting, and anomaly detection . o Working knowledge of cloud platforms (preferably AWS) and data lake architectures . o Strong data wrangling, cleaning, and schema design skills . o Ability to effectively communicate in English, both written and spoken . o A collaborative, team-oriented attitude with a proactive mindset . Bonus Points For: . Experience with Scala, graph analytics, or handling IoT/sensor data . . Hands-on exposure to Angular, JavaScript, or other frontend technologies. . Experience working in Agile software environments (scrum, sprint planning, retrospectives). . Familiarity with Docker, Kubernetes, and GitLab CI/CD. . Knowledge of clean code, TDD, and software integration processes. What You'll Gain: . Collaborate with global product teams with 20+ years of technical excellence. . Work in a disciplined SAFe Agile environment that values both delivery and work-life balance. . Make meaningful contributions to product success in a transparent and empowering culture. . Build scalable platforms that support sophisticated modular applications. Join us and be yourself! We value your unique identity and perspective, recognizing that our strength comes from the diverse backgrounds, experiences, and thoughts of our team members. We are fully committed to providing equitable opportunities and building a workplace that reflects the diversity of society. We also support you in your personal and professional journey by providing resources to help you thrive. Come bring your authentic self and create a better tomorrow with us. Make your mark in our exciting world at Siemens. This role is based in Chennai and is an Individual contributor role. You might be required to visit other locations within India and outside. In return, you'll get the chance to work with teams impacting - and the shape of things to come. We're Siemens. A collection of over 379,000 minds building the future, one day at a time in over 200 countries. Find out more about Siemens careers at: www.siemens.com/careers

When you join Verizon You want more out of a career. A place to share your ideas freely — even if they’re daring or different. Where the true you can learn, grow, and thrive. At Verizon, we power and empower how people live, work and play by connecting them to what brings them joy. We do what we love — driving innovation, creativity, and impact in the world. Our V Team is a community of people who anticipate, lead, and believe that listening is where learning begins. In crisis and in celebration, we come together — lifting our communities and building trust in how we show up, everywhere & always. Want in? Join the #VTeamLife. What You Will Be Doing... Commercial Data & Analytics is part of the Verizon Global Services (VGS) organization. The team supports initiatives to uncover actionable insights that drive business value. As the resident expert you will identify business problems, draft hypotheses, define success criteria, size experiments, and analyze overall test results. You will also partner across different teams to ensure alignment on test parameters and data sourcing. As a member of this team, your responsibilities will include: Lead the experimentation lifecycle from end-to-end, including hypothesis formulation, experimental design (e.g., A/B, multivariate), implementation oversight, statistical analysis, and insightful recommendation. Serve as a thought leader and mentor on experimentation, A/B testing, and causal inference, establishing best practices and elevating the team's analytical capabilities Crafting high-quality experimentation deliverables including measurement plans, reporting requirements, and test results. Partner closely with product, marketing, and engineering teams to translate ambiguous business questions into precise, testable hypotheses. Champion statistical best practices throughout the organization, ensuring all tests are properly powered, results are interpreted correctly, and conclusions are statistically sound. Translate complex experimental results into clear, actionable business recommendations and communicate them effectively to stakeholders at all levels, including senior leadership. What We’re Looking For… You are a deeply curious and statistically-minded scientist who is passionate about finding truth in data. You are not just a modeler; you are a scientific method champion who understands that well-designed experiments are the most reliable way to drive impactful change. You Will Need To Have A Bachelor’s degree with four or more years of experience. Six or more years of progressive experience as a Data Analyst, with a proven track record of leading initiatives in A/B testing, experimentation, and conversion rate optimization. Deep expertise in statistical hypothesis testing (e.g., t-tests, ANOVA, Chi-squared), experimental design (A/B, MVT), and power/sample size calculations. Expert-level proficiency in Python or R, with extensive experience using statistical and data manipulation libraries (e.g., statsmodels, scipy, pandas in Python; dplyr, rstatix in R). Mastery of SQL for complex data extraction and cohort analysis from large-scale data warehouses. Exceptional ability to explain statistical concepts like p-values, confidence intervals, and statistical significance to non-technical stakeholders to drive data-informed decisions. Excellent collaboration skills, with a history of working effectively with cross-functional teams to design and execute tests. Even better if you have one or more of the following: Expertise in advanced causal inference methods (e.g., Difference-in-Differences, Regression Discontinuity, Propensity Score Matching). Practical experience with commercial or open-source experimentation platforms (e.g., Optimizely, Statsig, VWO, or building in-house tools). Knowledge of multi-armed bandit algorithms and their application in real-time optimization. Experience in a product-led growth (PLG) or e-commerce environment where A/B testing is a core driver of strategy. Experience with Bayesian statistics and its application to experimental analysis. Proficiency with cloud computing platforms (e.g., AWS, GCP, Azure) and version control using Git. Experience building insightful dashboards and reports in tools like Tableau, Looker, or Qlik to monitor experiment results. Where you’ll be working In this hybrid role, you'll have a defined work location that includes work from home and assigned office days set by your manager. Scheduled Weekly Hours 40 Equal Employment Opportunity Verizon is an equal opportunity employer. We evaluate qualified applicants without regard to race, gender, disability or any other legally protected characteristics.

When you join Verizon You want more out of a career. A place to share your ideas freely — even if they’re daring or different. Where the true you can learn, grow, and thrive. At Verizon, we power and empower how people live, work and play by connecting them to what brings them joy. We do what we love — driving innovation, creativity, and impact in the world. Our V Team is a community of people who anticipate, lead, and believe that listening is where learning begins. In crisis and in celebration, we come together — lifting our communities and building trust in how we show up, everywhere & always. Want in? Join the #VTeamLife. What You Will Be Doing... Commercial Data & Analytics is part of the Verizon Global Services (VGS) organization. The team supports initiatives to uncover actionable insights that drive business value. As the resident expert you will identify business problems, draft hypotheses, define success criteria, size experiments, and analyze overall test results. You will also partner across different teams to ensure alignment on test parameters and data sourcing. As a member of this team, your responsibilities will include: Lead the experimentation lifecycle from end-to-end, including hypothesis formulation, experimental design (e.g., A/B, multivariate), implementation oversight, statistical analysis, and insightful recommendation. Serve as a thought leader and mentor on experimentation, A/B testing, and causal inference, establishing best practices and elevating the team's analytical capabilities Crafting high-quality experimentation deliverables including measurement plans, reporting requirements, and test results. Partner closely with product, marketing, and engineering teams to translate ambiguous business questions into precise, testable hypotheses. Champion statistical best practices throughout the organization, ensuring all tests are properly powered, results are interpreted correctly, and conclusions are statistically sound. Translate complex experimental results into clear, actionable business recommendations and communicate them effectively to stakeholders at all levels, including senior leadership. What We’re Looking For… You are a deeply curious and statistically-minded scientist who is passionate about finding truth in data. You are not just a modeler; you are a scientific method champion who understands that well-designed experiments are the most reliable way to drive impactful change. You Will Need To Have A Bachelor’s degree with four or more years of experience. Six or more years of progressive experience as a Data Analyst, with a proven track record of leading initiatives in A/B testing, experimentation, and conversion rate optimization. Deep expertise in statistical hypothesis testing (e.g., t-tests, ANOVA, Chi-squared), experimental design (A/B, MVT), and power/sample size calculations. Expert-level proficiency in Python or R, with extensive experience using statistical and data manipulation libraries (e.g., statsmodels, scipy, pandas in Python; dplyr, rstatix in R). Mastery of SQL for complex data extraction and cohort analysis from large-scale data warehouses. Exceptional ability to explain statistical concepts like p-values, confidence intervals, and statistical significance to non-technical stakeholders to drive data-informed decisions. Excellent collaboration skills, with a history of working effectively with cross-functional teams to design and execute tests. Even better if you have one or more of the following: Expertise in advanced causal inference methods (e.g., Difference-in-Differences, Regression Discontinuity, Propensity Score Matching). Practical experience with commercial or open-source experimentation platforms (e.g., Optimizely, Statsig, VWO, or building in-house tools). Knowledge of multi-armed bandit algorithms and their application in real-time optimization. Experience in a product-led growth (PLG) or e-commerce environment where A/B testing is a core driver of strategy. Experience with Bayesian statistics and its application to experimental analysis. Proficiency with cloud computing platforms (e.g., AWS, GCP, Azure) and version control using Git. Experience building insightful dashboards and reports in tools like Tableau, Looker, or Qlik to monitor experiment results. Where you’ll be working In this hybrid role, you'll have a defined work location that includes work from home and assigned office days set by your manager. Scheduled Weekly Hours 40 Equal Employment Opportunity Verizon is an equal opportunity employer. We evaluate qualified applicants without regard to race, gender, disability or any other legally protected characteristics.

Chennai, India Hyderabad, India Bangalore, India Job ID: R-1083311 Apply prior to the end date: September 20th, 2025 When you join Verizon You want more out of a career. A place to share your ideas freely — even if they’re daring or different. Where the true you can learn, grow, and thrive. At Verizon, we power and empower how people live, work and play by connecting them to what brings them joy. We do what we love — driving innovation, creativity, and impact in the world. Our V Team is a community of people who anticipate, lead, and believe that listening is where learning begins. In crisis and in celebration, we come together — lifting our communities and building trust in how we show up, everywhere & always. Want in? Join the #VTeamLife. What you will be doing... Commercial Data & Analytics is part of the Verizon Global Services (VGS) organization. The team supports initiatives to uncover actionable insights that drive business value. As the resident expert you will identify business problems, draft hypotheses, define success criteria, size experiments, and analyze overall test results. You will also partner across different teams to ensure alignment on test parameters and data sourcing. As a member of this team, your responsibilities will include: Lead the experimentation lifecycle from end-to-end, including hypothesis formulation, experimental design (e.g., A/B, multivariate), implementation oversight, statistical analysis, and insightful recommendation. Serve as a thought leader and mentor on experimentation, A/B testing, and causal inference, establishing best practices and elevating the team's analytical capabilities Crafting high-quality experimentation deliverables including measurement plans, reporting requirements, and test results. Partner closely with product, marketing, and engineering teams to translate ambiguous business questions into precise, testable hypotheses. Champion statistical best practices throughout the organization, ensuring all tests are properly powered, results are interpreted correctly, and conclusions are statistically sound. Translate complex experimental results into clear, actionable business recommendations and communicate them effectively to stakeholders at all levels, including senior leadership. What we’re looking for… You are a deeply curious and statistically-minded scientist who is passionate about finding truth in data. You are not just a modeler; you are a scientific method champion who understands that well-designed experiments are the most reliable way to drive impactful change. You will need to have: A Bachelor’s degree with four or more years of experience. Six or more years of progressive experience as a Data Analyst, with a proven track record of leading initiatives in A/B testing, experimentation, and conversion rate optimization. Deep expertise in statistical hypothesis testing (e.g., t-tests, ANOVA, Chi-squared), experimental design (A/B, MVT), and power/sample size calculations. Expert-level proficiency in Python or R, with extensive experience using statistical and data manipulation libraries (e.g., statsmodels, scipy, pandas in Python; dplyr, rstatix in R). Mastery of SQL for complex data extraction and cohort analysis from large-scale data warehouses. Exceptional ability to explain statistical concepts like p-values, confidence intervals, and statistical significance to non-technical stakeholders to drive data-informed decisions. Excellent collaboration skills, with a history of working effectively with cross-functional teams to design and execute tests. Even better if you have one or more of the following: Expertise in advanced causal inference methods (e.g., Difference-in-Differences, Regression Discontinuity, Propensity Score Matching). Practical experience with commercial or open-source experimentation platforms (e.g., Optimizely, Statsig, VWO, or building in-house tools). Knowledge of multi-armed bandit algorithms and their application in real-time optimization. Experience in a product-led growth (PLG) or e-commerce environment where A/B testing is a core driver of strategy. Experience with Bayesian statistics and its application to experimental analysis. Proficiency with cloud computing platforms (e.g., AWS, GCP, Azure) and version control using Git. Experience building insightful dashboards and reports in tools like Tableau, Looker, or Qlik to monitor experiment results. Where you’ll be working In this hybrid role, you'll have a defined work location that includes work from home and assigned office days set by your manager. Scheduled Weekly Hours 40 Equal Employment Opportunity Verizon is an equal opportunity employer. We evaluate qualified applicants without regard to race, gender, disability or any other legally protected characteristics. Apply Now Save Saved Open sharing options Share Related Jobs Senior Engineer Consultant-Data Science Save Madhapur, India, +1 other location Technology Princ Engr-Data Engineering Save Madhapur, India Technology Engineer III Spec-Data Engineering Save Madhapur, India, +1 other location Technology Shaping the future. Connect with the best and brightest to help innovate and operate some of the world’s largest platforms and networks.

1) Data Scientist – Generative AI & ML Job Description Function: Data Science and Analysis → Data Science / Machine Learning Below 4 skill is must have Generative AI Python LLMs MLOps We are looking for an experienced and visionary Generative AI Architect with 12-15 years of experience in AI/ML, including hands-on work with LLMs (Large Language Models) and Generative AI solutionsIn this strategic technical leadership role, you will be responsible for designing and overseeing the development of advanced GenAI platforms and solutions that transform business operations and customer experiences. As the GenAI Architect, you will work closely with data scientists, ML engineers, product teams, and stakeholders to conceptualize, prototype, and scale generative AI use cases across the organization or client engagements. Responsibilities: Lead the design and development of scalable GenAI solutions leveraging LLMs, diffusion models, and multimodal architectures. Architect end-to-end pipelines involving prompt engineering, vector databases, retrieval-augmented generation (RAG), and LLM fine-tuning. Select and integrate foundational models (e. g., GPT, Claude, LLaMA, Mistralbased on business needs and technical constraints. Define GenAI architecture blueprints, best practices, and reusable components for rapid development and experimentation. Guide teams on model evaluation, inference optimization, and cost-effective scaling strategies. Stay current on the rapidly evolving GenAI landscape and assess emerging tools, APIs, and frameworks. Work with product owners, business leaders, and data teams to identify high-impact GenAI use cases across domains like customer support, content generation, document understanding, and code generation. Support PoCs, pilots, and production deployments of GenAI models in secure, compliant environments. Collaborate with MLOps and cloud teams to enable continuous delivery, monitoring, and governance of GenAI systems. Requirements: Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or related technical field. A PhD is a plus. 12-15 years in AI/ML and software engineering, with 3+ years focused on Generative AI and LLM-based architectures. Core Skills: Deep expertise in machine learning, natural language processing (NLP), and deep learning architectures. Hands-on experience with LLMs, transformers, fine-tuning techniques (LoRA, PEFT), and prompt engineering. Proficient in Python, with libraries/frameworks such as Hugging Face Transformers, LangChain, OpenAI API, PyTorch, TensorFlow. Experience with vector databases (e. g., Pinecone, FAISS, Weaviate) and RAG pipelines. Strong understanding of cloud-native AI architectures (AWS/GCP/Azure), containerization (Docker/Kubernetes), and API integration. Architectural & Leadership Skills: Proven ability to design and deliver scalable, secure, and efficient GenAI systems. Strong communication skills for cross-functional collaboration and stakeholder engagement. Ability to mentor engineering teams and drive innovation across the AI/ML ecosystem. Nice-to-Have: Experience with multimodal models (text + image/audio/video). Knowledge of AI governance, ethical AI, and compliance frameworks. Familiarity with MLOps practices for GenAI, including model versioning, drift detection, and performance monitoring. 2) Data Scientist – Classical ML Job description We are looking for a Senior Data Scientist who is passionate about solving complex business problems using data. The ideal candidate will have strong hands-on experience in Python, SQL, and advanced machine learning algorithms, along with domain expertise in forecasting, pricing optimization, or inventory planning. You will work closely with cross-functional teams including product, engineering, and business stakeholders to drive data-driven strategies. Classical Ml - Linear reg, random forest, time series, decission trees, xg boost, logistic reg Key Responsibilities: Design, build, and deploy predictive and optimization models for use cases such as demand forecasting, dynamic pricing, or inventory optimization. Translate business problems into analytical frameworks and provide actionable insights. Build and maintain scalable data pipelines and model workflows using Python and SQL. Collaborate with data engineers to ensure model integration into production systems. Present findings and recommendations to leadership using data visualizations and storytelling. Stay current with the latest techniques in machine learning, statistics, and operations research. Lead and mentor junior data scientists and analysts when required. Required Skills and Experience: 5+ years of experience in a Data Science or Analytics role with a strong business impact. Strong programming skills in Python (pandas, scikit-learn, statsmodels, etc.). Proficiency in SQL for data extraction, transformation, and manipulation. Deep understanding of machine learning algorithms (regression, classification, clustering, time series forecasting). Experience with one or more of the following domains: Demand Forecasting Price Optimization Inventory Optimization / Supply Chain Analytics Strong problem-solving and quantitative skills. Experience working with large datasets and distributed computing tools (e.g., Spark, Hadoop) is a plus. Familiarity with data visualization tools (e.g., Tableau, Power BI, matplotlib, seaborn).

At EY, you'll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we're counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. EY GDS - AI and DATA - Statistical Modeler-Senior At EY, you'll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we're counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. As part of our EY- GDS AI and Data team, we help our clients solve complex business challenges with the help of data and technology. We dive deep into data to extract the greatest value and discover opportunities in key business and functions like Banking, Insurance, Manufacturing, Healthcare, Retail, Manufacturing and Auto, Supply Chain, and Finance. Technical Skills: . Statistical Programming Languages: Python, R . Libraries & Frameworks: Pandas, NumPy, Scikit-learn, StatsModels, Tidyverse, caret . Data Manipulation Tools: SQL, Excel . Data Visualization Tools: Matplotlib, Seaborn, ggplot2, . Machine Learning Techniques: Supervised and unsupervised learning, model evaluation (cross-validation, ROC curves) . 5-7 years of experience in building statistical forecast models for pharma industry . Deep understanding of patient flows,treatment journey across both Onc and Non Onc Tas. What we look for . A Team of people with commercial acumen, technical experience and enthusiasm to learn new things in this fast-moving environment What working at EY offers At EY, we're dedicated to helping our clients, from startups to Fortune 500 companies - and the work we do with them is as varied as they are. You get to work with inspiring and meaningful projects. Our focus is education and coaching alongside practical experience to ensure your personal development. We value our employees, and you will be able to control your own development with an individual progression plan. You will quickly grow into a responsible role with challenging and stimulating assignments. Moreover, you will be part of an interdisciplinary environment that emphasizes high quality and knowledge exchange. Plus, we offer: . Support, coaching and feedback from some of the most engaging colleagues around . Opportunities to develop new skills and progress your career . The freedom and flexibility to handle your role in a way that's right for you About EY As a global leader in assurance, tax, transaction and advisory services, we're using the finance products, expertise and systems we've developed to build a better working world. That starts with a culture that believes in giving you the training, opportunities and creative freedom to make things better. Whenever you join, however long you stay, the exceptional EY experience lasts a lifetime. EY | Building a better working world EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets. Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate. Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.

We are seeking an Full Stack Developer (Associate Consultant) to join our India team based in Gurgaon. This role at Viscadia offers a unique opportunity to gain hands-on experience in the healthcare industry, with comprehensive training in core consulting skills such as critical thinking, market analysis, and executive communication. Through project work and direct mentorship, you will develop a deep understanding of healthcare business dynamics and build a strong foundation for a successful consulting career. ROLES AND RESPONSIBILITIES Technical Responsibilities Design and build full-stack forecasting and simulation platforms using modern web technologies (e.g., React, Node.js, Python) hosted on AWS infrastructure (e.g., Lambda, EC2, S3, RDS, API Gateway). Automate data pipelines and model workflows using Python for data preprocessing, time-series modeling (e.g., ARIMA, Exponential Smoothing), and backend services. Replace legacy Excel/VBA tools with scalable, cloud-native applications, integrating dynamic reporting features and user controls via web UI. Use SQL and cloud databases (e.g., AWS RDS, Redshift) to query and transform large datasets as inputs to models and dashboards. Develop interactive web dashboards using frameworks like React + D3.js or embed tools like Power BI/Tableau into web portals to communicate insights effectively. Implement secure, modular APIs and microservices to support modularity, scalability, and seamless data exchange across platforms. Ensure cost-effective and reliable deployment of solutions via AWS services, CI/CD pipelines, and infrastructure-as-code (e.g., CloudFormation, Terraform). Business Responsibilities Support the development and enhancement of forecasting and analytics platforms tailored to the needs of pharmaceutical clients across various therapeutic areas Build in depth understanding of pharma forecasting concepts, disease areas, treatment landscapes, and market dynamics to contextualize forecasting models and inform platform features Partner with cross-functional teams to ensure forecast deliverables align with client objectives, timelines, and decision-making needs Contribute to a culture of knowledge sharing and continuous improvement by mentoring junior team members and helping codify best practices in forecasting and business analytics Grow into a client-facing role, combining an understanding of commercial strategy with forecasting expertise to lead engagements and drive value for clients QUALIFICATIONS Bachelor’s degree (B.Tech/B.E.) from a premier engineering institute, preferably in Computer Science, Information Technology, Electrical Engineering, or related disciplines 2+ years of experience in full-stack development, with a strong focus on designing, developing, and maintaining AWS-based applications and services SKILLS AND TECHNICAL PROFICIENCIES Technical Skills Proficient in Python, with practical experience using libraries such as pandas, NumPy, matplotlib/seaborn, and statsmodels for data analysis and statistical modeling Strong command of SQL for data querying, transformation, and seamless integration with backend systems Hands-on experience in designing and maintaining ETL/ELT data pipelines, ensuring efficient and scalable data workflows Solid understanding and applied experience with cloud platforms, particularly AWS; working familiarity with Azure and Google Cloud Platform (GCP) Full-stack web development expertise, including building and deploying modern web applications, web hosting, and API integration Proficient in Microsoft Excel and PowerPoint, with advanced skills in data visualization and delivering professional presentations Soft Skills Excellent verbal and written communication skills, with the ability to effectively engage both technical and non-technical stakeholders Strong analytical thinking and problem-solving abilities, with a structured and solution-oriented mindset Demonstrated ability to work independently as well as collaboratively within cross-functional teams Adaptable and proactive, with a willingness to thrive in a dynamic, fast-growing environment Genuine passion for consulting, with a focus on delivering tangible business value for clients Domain Expertise Strong understanding of pharmaceutical commercial models, including treatment journeys, market dynamics, and key therapeutic areas Experience working with and interpreting industry-standard datasets such as IQVIA, Symphony Health, or similar secondary data sources Familiarity with product lifecycle management, market access considerations, and sales performance tracking metrics used across the pharmaceutical value chain

At American Express, our culture is built on a 175-year history of innovation, shared values and Leadership Behaviors, and an unwavering commitment to back our customers, communities, and colleagues. As part of Team Amex, you'll experience this powerful backing with comprehensive support for your holistic well-being and many opportunities to learn new skills, develop as a leader, and grow your career. Here, your voice and ideas matter, your work makes an impact, and together, you will help us define the future of American Express. How will you make an impact in this role? This Position is in GSG Advanced Analytics team as part of GSG MIS COE, is looking for full time candidates as Data Science Analysts. The Advanced Analytics team works across multiple Data Science/Machine Learning portfolio of projects across GSG organization across multiple areas of Servicing. Understand the overall business perspective and help conceptualize the business problem into a Data Science/ML roadmap Have a research bend of mind and read/engage/apply new techniques and algorithms in the field to generate efficiencies/process improvements. Rigorous testing of algorithms as per business norms and delivering significant working leverage over status quo and generate value for the business. Capability of writing, debugging and compiling codes in multiple Machine Learning environment and understanding of Python stack is necessary Research focus on problem solving through NLP(Natural Language Processing) is critical. Clear application on performance testing and validation framework of machine learning models Understand and deploy mathematical foundations of cutting edge NLP techniques on varied sources of data Willingness to derive insights from terabyte sized data and capability to design scalable solutions is paramount Minimum Qualifications Proven experience of applying using Machine Learning techniques like Regression, Classification, Supervised or Unsupervised Recommenders, Deep Learning etc. Ability to work in cross functional teams Excellent data wrangling and visualization skills Hands on knowledge of SQL is expected A research mindset with a zeal to experiment new algorithms is expected Preferred Qualifications: Master’s in a quantitative field (e.g., Finance, Engineering, Mathematics, Statistics Computer Science or Economics) from a top institute. A set of high impact research papers on applied NLP/ Deep Learning/ GenAI Prior experience working with Transformers/LSTMs/CNNs preferred Working knowledge of finetuning Large Language Models(LLMs) is a plus Deep knowledge of Statistics and Mathematics and ability to dissect problems from the first principle. Exposure to fields like Linear Algebra, Bayesian Statistics, Group theory is desirable Complete grip on Python environment and libraries (pandas, numpy, nltk, statsmodels, gensim, pyspark, spacy, transformers), Deep learning expertise is preferred and any of Tensorflow/Torch is preferred. We back you with benefits that support your holistic well-being so you can be and deliver your best. This means caring for you and your loved ones' physical, financial, and mental health, as well as providing the flexibility you need to thrive personally and professionally: Competitive base salaries Bonus incentives Support for financial-well-being and retirement Comprehensive medical, dental, vision, life insurance, and disability benefits (depending on location) Flexible working model with hybrid, onsite or virtual arrangements depending on role and business need Generous paid parental leave policies (depending on your location) Free access to global on-site wellness centers staffed with nurses and doctors (depending on location) Free and confidential counseling support through our Healthy Minds program Career development and training opportunities American Express is an equal opportunity employer and makes employment decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, disability status, age, or any other status protected by law. Offer of employment with American Express is conditioned upon the successful completion of a background verification check, subject to applicable laws and regulations.

AB InBev GCC was incorporated in 2014 as a strategic partner for Anheuser-Busch InBev. The center leverages the power of data and analytics to drive growth for critical business functions such as operations, finance, people, and technology. The teams are transforming Operations through Tech and Analytics. Do You Dream Big? We Need You. Job Description Job Title: Senior Data Scientist Location: Bangalore Reporting to: Senior Manager 1. Purpose of the role This role sits at the intersection of data science and revenue growth strategy, focused on developing advanced analytical solutions to optimize pricing, trade promotions, and product mix. The candidate will lead the end-to-end design, deployment, and automation of machine learning models and statistical frameworks that support commercial decision-making, predictive scenario planning, and real-time performance tracking. By leveraging internal and external data sources—including transactional, market, and customer-level data—this role will deliver insights into price elasticity, promotional lift, channel efficiency, and category dynamics. The goal is to drive measurable improvements in gross margin, ROI on trade spend, and volume growth through data-informed strategies. 2. Key tasks & accountabilities Design and implement price elasticity models using linear regression, log-log models, and hierarchical Bayesian frameworks to understand consumer response to pricing changes across channels and segments. Build uplift models (e.g., Linear Regression, XGBoost for treatment effect) to evaluate promotional effectiveness and isolate true incremental sales vs. base volume. Develop demand forecasting models using ARIMA, SARIMAX, and Prophet, integrating external factors such as seasonality, promotions, and competitor activity, time-series clustering and k-means segmentation to group SKUs, customers, and geographies for targeted pricing and promotion strategies. Construct assortment optimization models using conjoint analysis, choice modeling, and market basket analysis to support category planning and shelf optimization. Use Monte Carlo simulations and what-if scenario modeling to assess revenue impact under varying pricing, promo, and mix conditions. Conduct hypothesis testing (t-tests, ANOVA, chi-square) to evaluate statistical significance of pricing and promotional changes. Create LTV (lifetime value) and customer churn models to prioritize trade investment decisions and drive customer retention strategies. Integrate Nielsen, IRI, and internal POS data to build unified datasets for modeling and advanced analytics in SQL, Python (pandas, statsmodels, scikit-learn), and Azure Databricks environments. Automate reporting processes and real-time dashboards for price pack architecture (PPA), promotion performance tracking, and margin simulation using advanced Excel and Python. Lead post-event analytics using pre/post experimental designs, including difference-in-differences (DiD) methods to evaluate business interventions. Collaborate with Revenue Management, Finance, and Sales leaders to convert insights into pricing corridors, discount policies, and promotional guardrails. Translate complex statistical outputs into clear, executive-ready insights with actionable recommendations for business impact. Continuously refine model performance through feature engineering, model validation, and hyperparameter tuning to ensure accuracy and scalability. Provide mentorship to junior analysts, enhancing their skills in modeling, statistics, and commercial storytelling. Maintain documentation of model assumptions, business rules, and statistical parameters to ensure transparency and reproducibility. Other Competencies Required Presentation Skills: Effectively presenting findings and insights to stakeholders and senior leadership to drive informed decision-making. Collaboration: Working closely with cross-functional teams, including marketing, sales, and product development, to implement insights-driven strategies. Continuous Improvement: Actively seeking opportunities to enhance reporting processes and insights generation to maintain relevance and impact in a dynamic market environment. Data Scope Management: Managing the scope of data analysis, ensuring it aligns with the business objectives and insights goals. Act as a steadfast advisor to leadership, offering expert guidance on harnessing data to drive business outcomes and optimize customer experience initiatives. Serve as a catalyst for change by advocating for data-driven decision-making and cultivating a culture of continuous improvement rooted in insights gleaned from analysis. Continuously evaluate and refine reporting processes to ensure the delivery of timely, relevant, and impactful insights to leadership stakeholders while fostering an environment of ownership, collaboration, and mentorship within the team. Business Environment Main Characteristics: Work closely with Zone Revenue Management teams. Work in a fast-paced environment. Provide proactive communication to the stakeholders. This is an offshore role and requires comfort with working in a virtual environment. GCC is referred to as the offshore location. The role requires working in a collaborative manner with Zone/country business heads and GCC commercial teams. Summarize insights and recommendations to be presented back to the business. Continuously improve, automate, and optimize the process. Geographical Scope : Europe 3. Qualifications, Experience, Skills Level of educational attainment required: Bachelor or Post-Graduate in the field of Business & Marketing, Engineering/Solution, or other equivalent degree or equivalent work experience. MBA/Engg. in a relevant technical field such as Marketing/Finance. Extensive experience solving business problems using quantitative approaches. Comfort with extracting, manipulating, and analyzing complex, high volume, high dimensionality data from varying sources. Previous work experience required 5-8 years of experience in the Retail/CPG domain. Technical skills required Data Manipulation & Analysis: Advanced proficiency in SQL, Python (Pandas, NumPy), and Excel for structured data processing. Data Visualization: Expertise in Power BI and Tableau for building interactive dashboards and performance tracking tools. Modeling & Analytics: Hands-on experience with regression analysis, time series forecasting, and ML models using scikit-learn or XGBoost. Data Engineering Fundamentals: Knowledge of data pipelines, ETL processes, and integration of internal/external datasets for analytical readiness. Proficient in Python (pandas, scikit-learn, statsmodels), SQL, and Power BI. Skilled in regression, Bayesian modeling, uplift modeling, time-series forecasting (ARIMA, SARIMAX, Prophet), and clustering (k-means). Strong grasp of hypothesis testing, model validation, and scenario simulation. And above all of this, an undying love for beer! We dream big to create future with more cheers .

About Hakkoda Hakkoda, an IBM Company, is a modern data consultancy that empowers data driven organizations to realize the full value of the Snowflake Data Cloud. We provide consulting and managed services in data architecture, data engineering, analytics and data science. We are renowned for bringing our clients deep expertise, being easy to work with, and being an amazing place to work! We are looking for curious and creative individuals who want to be part of a fast-paced, dynamic environment, where everyone’s input and efforts are valued. We hire outstanding individuals and give them the opportunity to thrive in a collaborative atmosphere that values learning, growth, and hard work. Our team is distributed across North America, Latin America, India and Europe. If you have the desire to be a part of an exciting, challenging, and rapidly-growing Snowflake consulting services company, and if you are passionate about making a difference in this world, we would love to talk to you!. We are seeking an exceptional and highly motivated Lead Data Scientist with a PhD in Data Science, Computer Science, Applied Mathematics, Statistics, or a closely related quantitative field, to spearhead the design, development, and deployment of an automotive OEM’s next-generation Intelligent Forecast Application. This pivotal role will leverage cutting-edge machine learning, deep learning, and statistical modeling techniques to build a robust, scalable, and accurate forecasting system crucial for strategic decision-decision-making across the automotive value chain, including demand planning, production scheduling, inventory optimization, predictive maintenance, and new product introduction. The successful candidate will be a recognized expert in advanced forecasting methodologies, possess a strong foundation in data engineering and MLOps principles, and demonstrate a proven ability to translate complex research into tangible, production-ready applications within a dynamic industrial environment. This role demands not only deep technical expertise but also a visionary approach to leveraging data and AI to drive significant business impact for a leading automotive OEM. Role Description Strategic Leadership & Application Design: Lead the end-to-end design and architecture of the Intelligent Forecast Application, defining its capabilities, modularity, and integration points with existing enterprise systems (e.g., ERP, SCM, CRM). Develop a strategic roadmap for forecasting capabilities, identifying opportunities for innovation and the adoption of emerging AI/ML techniques (e.g., generative AI for scenario planning, reinforcement learning for dynamic optimization). Translate complex business requirements and automotive industry challenges into well-defined data science problems and technical specifications. Advanced Model Development & Research: Design, develop, and validate highly accurate and robust forecasting models using a variety of advanced techniques, including: Time Series Analysis: ARIMA, SARIMA, Prophet, Exponential Smoothing, State-space models. Machine Learning: Gradient Boosting (XGBoost, LightGBM), Random Forests, Support Vector Machines. Deep Learning: LSTMs, GRUs, Transformers, and other neural network architectures for complex sequential data. Probabilistic Forecasting: Quantile regression, Bayesian methods to capture uncertainty. Hierarchical & Grouped Forecasting: Managing forecasts across multiple product hierarchies, regions, and dealerships. Incorporate diverse data sources, including historical sales, market trends, economic indicators, competitor data, internal operational data (e.g., production schedules, supply chain disruptions), external events, and unstructured data. Conduct extensive exploratory data analysis (EDA) to identify patterns, anomalies, and key features influencing automotive forecasts. Stay abreast of the latest academic researchand industry advancements in forecasting, machine learning, and AI, actively evaluating and advocating for their practical application within the OEM. Application Development & Deployment (MLOps): Architect and implement scalable data pipelines for ingestion, cleaning, transformation, and feature engineering of large, complex automotive datasets. Develop robust and efficient code for model training, inference, and deployment within a production environment. Implement MLOps best practices for model versioning, monitoring, retraining, and performance management to ensure the continuous accuracy and reliability of the forecasting application. Collaborate closely with Data Engineering, Software Development, and IT Operations teams to ensure seamless integration, deployment, and maintenance of the application. Performance Evaluation & Optimization: Define and implement rigorous evaluation metrics for forecasting accuracy (e.g., MAE, RMSE, MAPE, sMAPE, wMAPE, Pinball Loss) and business impact. Perform A/B testing and comparative analyses of different models and approaches to continuously improve forecasting performance. Identify and mitigate sources of bias and uncertainty in forecasting models. Collaboration & Mentorship: Work cross-functionally with various business units (e.g., Sales, Marketing, Supply Chain, Manufacturing, Finance, Product Development) to understand their forecasting needs and integrate solutions. Communicate complex technical concepts and model insights clearly and concisely to both technical and non-technical stakeholders. Provide technical leadership and mentorship to junior data scientists and engineers, fostering a culture of innovation and continuous learning. Potentially contribute to intellectual property (patents) and present findings at internal and external conferences. Qualifications Education: PhD in Data Science, Computer Science, Statistics, Applied Mathematics, Operations Research, or a closely related quantitative field. Experience: 5+ years of hands-on experience in a Data Scientist or Machine Learning Engineer role, with a significant focus on developing and deploying advanced forecasting solutions in a production environment. Demonstrated experience designing and developing intelligent applications, not just isolated models. Experience in the automotive industry or a similar complex manufacturing/supply chain environment is highly desirable. Technical Skills: Expert proficiency in Python (Numpy, Pandas, Scikit-learn, Statsmodels) and/or R. Strong proficiency in SQL. Machine Learning/Deep Learning Frameworks: Extensive experience with TensorFlow, PyTorch, Keras, or similar deep learning libraries. Forecasting Specific Libraries: Proficiency with forecasting libraries like Prophet, Statsmodels, or specialized time series packages. Data Warehousing & Big Data Technologies: Experience with distributed computing frameworks (e.g., Apache Spark, Hadoop) and data storage solutions (e.g., Snowflake, Databricks, S3, ADLS). Cloud Platforms: Hands-on experience with at least one major cloud provider (Azure, AWS, GCP) for data science and ML deployments. MLOps: Understanding and practical experience with MLOps tools and practices (e.g., MLflow, Kubeflow, Docker, Kubernetes, CI/CD pipelines). Data Visualization: Proficiency with tools like Tableau, Power BI, or similar for creating compelling data stories and dashboards. Analytical Prowess: Deep understanding of statistical inference, experimental design, causal inference, and the mathematical foundations of machine learning algorithms. Problem Solving: Proven ability to analyze complex, ambiguous problems, break them down into manageable components, and devise innovative solutions. Preferred Qualifications Publications in top-tier conferences or journals related to forecasting, time series analysis, or applied machine learning. Experience with real-time forecasting systems or streaming data analytics. Familiarity with specific automotive data types (e.g., telematics, vehicle sensor data, dealership data, market sentiment). Experience with distributed version control systems (e.g., Git). Knowledge of agile development methodologies. Soft Skills Exceptional Communication: Ability to articulate complex technical concepts and insights to a diverse audience, including senior management and non-technical stakeholders. Collaboration: Strong interpersonal skills and a proven ability to work effectively within cross-functional teams. Intellectual Curiosity & Proactiveness: A passion for continuous learning, staying ahead of industry trends, and proactively identifying opportunities for improvement. Strategic Thinking: Ability to see the big picture and align technical solutions with overall business objectives. Mentorship: Desire and ability to guide and develop less experienced team members. Resilience & Adaptability: Thrive in a fast-paced, evolving environment with complex challenges. Benefits Health Insurance Paid leave Technical training and certifications Robust learning and development opportunities Incentive Toastmasters Food Program Fitness Program Referral Bonus Program Hakkoda is committed to fostering diversity, equity, and inclusion within our teams. A diverse workforce enhances our ability to serve clients and enriches our culture. We encourage candidates of all races, genders, sexual orientations, abilities, and experiences to apply, creating a workplace where everyone can succeed and thrive. Ready to take your career to the next level? 🚀 💻 Apply today👇 and join a team that’s shaping the future!! Hakkoda is an IBM subsidiary which has been acquired by IBM and will be integrated in the IBM organization. Hakkoda will be the hiring entity. By Proceeding with this application, you understand that Hakkoda will share your personal information with other IBM subsidiaries involved in your recruitment process, wherever these are located. More information on how IBM protects your personal information, including the safeguards in case of cross-border data transfer, are available here.

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. EY GDS – AI and DATA – Statistical Modeler-Senior At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. As part of our EY- GDS AI and Data team, we help our clients solve complex business challenges with the help of data and technology. We dive deep into data to extract the greatest value and discover opportunities in key business and functions like Banking, Insurance, Manufacturing, Healthcare, Retail, Manufacturing and Auto, Supply Chain, and Finance. Technical Skills: Statistical Programming Languages: Python, R Libraries & Frameworks: Pandas, NumPy, Scikit-learn, StatsModels, Tidyverse, caret Data Manipulation Tools: SQL, Excel Data Visualization Tools: Matplotlib, Seaborn, ggplot2, Machine Learning Techniques: Supervised and unsupervised learning, model evaluation (cross-validation, ROC curves) 5-7 years of experience in building statistical forecast models for pharma industry Deep understanding of patient flows,treatment journey across both Onc and Non Onc Tas. What We Look For A Team of people with commercial acumen, technical experience and enthusiasm to learn new things in this fast-moving environment What Working At EY Offers At EY, we’re dedicated to helping our clients, from startups to Fortune 500 companies — and the work we do with them is as varied as they are. You get to work with inspiring and meaningful projects. Our focus is education and coaching alongside practical experience to ensure your personal development. We value our employees, and you will be able to control your own development with an individual progression plan. You will quickly grow into a responsible role with challenging and stimulating assignments. Moreover, you will be part of an interdisciplinary environment that emphasizes high quality and knowledge exchange. Plus, we offer: Support, coaching and feedback from some of the most engaging colleagues around Opportunities to develop new skills and progress your career The freedom and flexibility to handle your role in a way that’s right for you About EY As a global leader in assurance, tax, transaction and advisory services, we’re using the finance products, expertise and systems we’ve developed to build a better working world. That starts with a culture that believes in giving you the training, opportunities and creative freedom to make things better. Whenever you join, however long you stay, the exceptional EY experience lasts a lifetime. EY | Building a better working world EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets. Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate. Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. EY GDS – AI and DATA – Statistical Modeler-Senior At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. As part of our EY- GDS AI and Data team, we help our clients solve complex business challenges with the help of data and technology. We dive deep into data to extract the greatest value and discover opportunities in key business and functions like Banking, Insurance, Manufacturing, Healthcare, Retail, Manufacturing and Auto, Supply Chain, and Finance. Technical Skills: Statistical Programming Languages: Python, R Libraries & Frameworks: Pandas, NumPy, Scikit-learn, StatsModels, Tidyverse, caret Data Manipulation Tools: SQL, Excel Data Visualization Tools: Matplotlib, Seaborn, ggplot2, Machine Learning Techniques: Supervised and unsupervised learning, model evaluation (cross-validation, ROC curves) 5-7 years of experience in building statistical forecast models for pharma industry Deep understanding of patient flows,treatment journey across both Onc and Non Onc Tas. What We Look For A Team of people with commercial acumen, technical experience and enthusiasm to learn new things in this fast-moving environment What Working At EY Offers At EY, we’re dedicated to helping our clients, from startups to Fortune 500 companies — and the work we do with them is as varied as they are. You get to work with inspiring and meaningful projects. Our focus is education and coaching alongside practical experience to ensure your personal development. We value our employees, and you will be able to control your own development with an individual progression plan. You will quickly grow into a responsible role with challenging and stimulating assignments. Moreover, you will be part of an interdisciplinary environment that emphasizes high quality and knowledge exchange. Plus, we offer: Support, coaching and feedback from some of the most engaging colleagues around Opportunities to develop new skills and progress your career The freedom and flexibility to handle your role in a way that’s right for you About EY As a global leader in assurance, tax, transaction and advisory services, we’re using the finance products, expertise and systems we’ve developed to build a better working world. That starts with a culture that believes in giving you the training, opportunities and creative freedom to make things better. Whenever you join, however long you stay, the exceptional EY experience lasts a lifetime. EY | Building a better working world EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets. Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate. Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.