1345 Data Scientist Jobs - Page 46

Conduct cutting-edge research and develop advanced NLP algorithms and models. Build and fine-tune deep learning and machine learning models, with a focus on large language models. Work closely with internal stakeholders to define model requirements and ensure alignment with business objectives. Develop AI predictive models and perform data and model accuracy analyses. Produce and present findings, technical concepts, and model recommendations to both technical and non-technical stakeholders. Develop and maintain scripts/tools to automate both new model production and updates to existing model packages. Stay abreast of the latest advancements in data science research and contribute to the development of our knowledge base. Collaborate with developers to design automation and tool improvements for model building. Maintain documentation of processes and projects across all supported languages and environments. Have you got what it takes? Masters degree in the field of Computer Science, Technology, Engineering, Math, or equivalent practical experience Minimum of 4-7 years of data science work experience, including implementing machine learning and NLP models using real-life data. Strong research skills, with a proven track record of developing and implementing advanced machine learning algorithms. Advanced knowledge of supervised and unsupervised machine learning algorithms. Excellent proficiency in Python programming. Experience with deep learning models and libraries such as PyTorch, TensorFlow, Hands-on experience with transformer models and Gen AI frameworks (HuggingFace, AWS Bedrock, Azure Foundry ). Strong verbal and written communication skills, including effective presentation abilities. Ability to work independently and as part of a team, demonstrating analytical thinking and problem-solving skills. You will have an advantage if you also have: Experience deploying models in production environments, ensuring scalability and performance using AWS/GCP/Azure Familiarity with relational databases and query languages (eg, MSSQL) and basic SQL knowledge. Experience with Retrieval-Augmented Generation (RAG) pipelines Understanding of multimodal models (text, audio, vision). Experience in Customer Experience domains. Experience working on international, globe-spanning teams. Past participation in a formal research setting. Experience as part of a software organization

As a Staff Data Scientist for Walmart Global Tech, you'll have the opportunity to Drive data-derived insights across a wide range of retail ; Finance divisions by developing advanced statistical models, machine learning algorithms and computational algorithms based on business initiatives Direct the gathering of data, assess data validity and synthesize data into large analytics datasets to support project goals Utilize big data analytics and advanced data science techniques to identify trends, patterns, and discrepancies in data. Determine additional data needed to support insights Build and train AI/ML models for replication for future projects Deploy and maintain the data science solutions Communicate recommendations to business partners and influence future plans based on insights Consult with business stakeholders regarding algorithm-based recommendations and be a thought-leader to develop these into business actions. Closely partners with the Senior Manager ; Director of Data Science to drive data science adoption in the domain Guides. data scientists, senior data scientists ; staff data scientists across multiple sub-domains to ensure on-time delivery of ML products Drive efficiency across the domain in terms of DS and ML best practices, ML Ops practices, resource utilization, reusability and multi-tenancy. Lead multiple complex ML products and guide senior tech leads in the domain in efficiently leading their products. Drive synergies across different products in terms of algorithmic innovation and sharing of best practices. Proactive identification of complex business problems that can be solved using advanced ML, finding opportunities and gaps in the current business domain Evaluates proposed business cases for projects and initiatives What You Will Bring Masters with > 10 years OR Ph.D. with > 8 years of relevant experience. Educational qualifications should be Computer Science/Statistics/Mathematics or a related area. Minimum 6 years of experience as a data science technical lead Ability to lead multiple data science projects end to end. Deep experience in building data science solution in areas like fraud prevention, forecasting, shrink and waste reduction, inventory management, recommendation, assortment and price optimization Deep experience in simultaneously leading multiple data science initiatives end to end from translating business needs to analytical asks, leading the process of building solutions and the eventual act of deployment and maintenance of them Strong experience in machine learning: Classification models, regression models, NLP, Forecasting, Unsupervised models, Optimization, Graph ML, Causal inference, Causal ML, Statistical Learning, experimentation ; Gen-AI In Gen-AI, it is desirable to have experience in embedding generation from training materials, storage and retrieval from Vector Databases, set-up and provisioning of managed LLM gateways, development of Retrieval augmented generation based LLM agents, model selection, iterative prompt engineering and finetuning based on accuracy and user-feedback, monitoring and governance. Ability to scale and deploy data science solutions. Strong Experience with one or more of Python and R. Experience in GCP/Azure Strong Experience in Python, PySpark Google Cloud platform, Vertex AI, Kubeflow, model deployment Strong Experience with big data platforms Hadoop (Hive, Map Reduce, HQL, Scala) Experience with GPU/CUDA for computational efficiency Minimum Qualifications... Minimum Qualifications:Option 1: Bachelors degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 4 years experience in an analytics related field. Option 2: Masters degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 2 years experience in an analytics related field. Option 3: 6 years experience in an analytics or related field.

we're hiring a Staff Scientist for Uber s Direct, Connect, and Grocery Delivery team three of the company s fastest-growing business lines redefining the future of on-demand logistics. From retail returns and prescription deliveries to nationwide shipments and grocery fulfillment, this team empowers customers to get anything delivered, anytime. In this role, you'll lead the scientific strategy to create a best-in-class user experience across Uber s delivery platforms. you'll drive both merchant and consumer growth through funnel analysis, user targeting, promotion optimization, and SKU-level insights ensuring every decision is grounded in rigorous, data-driven thinking. we're looking for someone with deep analytical expertise and a strong business mindset an individual who can translate complex data into clear, actionable insights and help shape the strategic direction of our products. What you'll Do: Serve as Science Tech Lead (IC) for a team based in India, supporting Uber s Direct, Connect, and Grocery Delivery business. Apply advanced analytics, including statistical modeling, and causal inference, to improve delivery experiences. Translate complex data into actionable insights for executives and cross-functional stakeholders. Collaborate with Product, Engineering, and Ops to drive a data-informed product roadmap. Mentor scientists and promote best practices in data science and machine learning. Foster a culture of scientific rigor and strong cross-functional collaboration. What you'll Need: Undergraduate and/or graduate degree in Statistics, Machine Learning, Operations Research, or other quantitative fields. 9+ years of industry experience as an (Applied / Data) Scientist or equivalent. Experience with exploratory data analysis, statistical analysis and testing, causal analysis and ML model development. Experience in experimental design and analysis. Proficiency using Python, Spark at scale with large data sets. Experience with tools like SQL, R in a production environment.

Alegeus is looking for Expert Data Scientist I to join our dynamic team and embark on a rewarding career journey Undertaking data collection, preprocessing and analysis Building models to address business problems Presenting information using data visualization techniques Identify valuable data sources and automate collection processes Undertake preprocessing of structured and unstructured data Analyze large amounts of information to discover trends and patterns Build predictive models and machine-learning algorithms Combine models through ensemble modeling Present information using data visualization techniques Propose solutions and strategies to business challenges Collaborate with engineering and product development teams

We are seeking a talented Data Scientist II to join our team. The ideal candidate will have 2-5 years of experience in data science and possess expertise in machine learning, deep learning, Python programming, SQL, Amazon Redshift, NLP, and AWS Cloud. ** Duties and Responsibilities: Develop and implement machine learning models to extract insights from large datasets. Utilize deep learning techniques to enhance data analysis and predictive modeling. Write efficient Python code to manipulate and analyze data. - Work with SQL databases to extract and transform data for analysis. Utilize Amazon Redshift for data warehousing and analytics. Apply NLP techniques to extract valuable information from unstructured data. - Utilize AWS Cloud services for data storage, processing, and analysis. Qualifications and Requirements: Bachelors degree in Computer Science, Statistics, Mathematics, or related field. - 2-5 years of experience in data science or related field. Proficiency in machine learning, deep learning, Python programming, SQL, Amazon Redshift, NLP, and AWS Cloud. Strong analytical and problem-solving skills. - Excellent communication and teamwork abilities. * *Key Competencies - Strong analytical skills. - Problem-solving abilities. - Proficiency in machine learning and deep learning techniques. Excellent programming skills in Python. - Knowledge of SQL and database management. - Familiarity with Amazon Redshift, NLP, and AWS Cloud services. ** Performance Expectations: Develop and deploy advanced machine learning models. Extract valuable insights from complex datasets. Collaborate with cross-functional teams to drive data-driven decision-making. Stay updated on the latest trends and technologies in data science. We are looking for a motivated and skilled Data Scientist I to join our team and contribute to our data-driven initiatives. If you meet the qualifications and are passionate about data science, we encourage you to apply.

Developing and applying custom data models and algorithms to data sets Applying LLM models / internal tools to solve Business problems Preprocessing structured and unstructured data. Processing, cleansing, and validating the integrity of data to be used for analysis Data mining or extracting usable data from valuable data sources To be successful in this role, we re seeking the following: Bachelors degree in computer science engineering or a related discipline, or equivalent work experience required. 9 to 15 years of experience in Data Science Understanding of data preparation techniques, feature engineering techniques, and related ML algorithms Experience in data manipulation, data visualization and analysis: use of structured data tools (e.g., SQL) & proficiency in Python and common machine learning and AI frameworks and packages, such as Scikit-learn, TensorFlow, PyTorch, etc. Basic understanding of LLM architectures, such as GPT, BERT, or Transformer models. Experience in prompt engineering, large language model selection and API model inference. Experience in statistics and probability Experience with visualization tools (e.g. Qlik, Streamlit, Dash, PowerBI) Understanding of various evaluation metrics related to different algorithms

Join the Arium team at Verisk s Hyderabad, India office within Extreme Event Solutions As a data scientist, you will contribute to the building of emerging and systemic liability catastrophe models and products designed to address risk estimation in our ever-evolving world These catastrophe models rely on quantitative modeling and simulations of litigation outcomes To produce unbiased risk estimates, our models carefully consider historical data changes, supplemented by subject matter expertise, where statistical uncertainty exists Our products provide opportunities for creative problem-solving, data science, machine learning, and effective communication at the intersection of systemic liability risk, (re)insurance, and society By doing so, we are making a tangible impact in the cutting-edge field of catastrophe risk modeling

AIML Engineer, NLP, LLM, AWS, RAG Systems

Expert knowledge of statistics and probability theory Proficiency in at least one programming language such as Python, R, or SQL Advanced experience with data visualization tools such as Tableau or PowerBI Expert understanding of machine learning algorithms and models Advanced knowledge of data cleaning and preprocessing techniques Experience with data analysis and interpretation EXPERIENCE 8-11 Years SKILLS Primary Skill: Data Science Sub Skill(s): Data Science Additional Skill(s): Python, Data Science

Overview We are PepsiCo PepsiCo is one of the world's leading food and beverage companies with more than $79 Billion in Net Revenue and a global portfolio of diverse and beloved brands. We have a complementary food and beverage portfolio that includes 22 brands that each generate more than $1 Billion in annual retail sales. PepsiCo's products are sold in more than 200 countries and territories around the world. PepsiCo's strength is its people. We are over 250,000 game changers, mountain movers and history makers, located around the world, and united by a shared set of values and goals. We believe that acting ethically and responsibly is not only the right thing to do, but also the right thing to do for our business. At PepsiCo, we aim to deliver top-tier financial performance over the long term by integrating sustainability into our business strategy, leaving a positive imprint on society and the environment. We call this Winning with Pep+ Positive . For more information on PepsiCo and the opportunities it holds, visit www.pepsico.com. PepsiCo Data Analytics & AI Overview: With data deeply embedded in our DNA, PepsiCo Data, Analytics and AI (DA&AI) transforms data into consumer delight. We build and organize business-ready data that allows PepsiCo’s leaders to solve their problems with the highest degree of confidence. Our platform of data products and services ensures data is activated at scale. This enables new revenue streams, deeper partner relationships, new consumer experiences, and innovation across the enterprise. The Data Science Pillar in DA&AI will be the organization where Data Scientist and ML Engineers report to in the broader D+A Organization. Also DS will lead, facilitate and collaborate on the larger DS community in PepsiCo. DS will provide the talent for the development and support of DS component and its life cycle within DA&AI Products. And will support “pre-engagement” activities as requested and validated by the prioritization framework of DA&AI. Data Scientist-Gurugram and Hyderabad The role will work in developing Machine Learning (ML) and Artificial Intelligence (AI) projects. Specific scope of this role is to develop ML solution in support of ML/AI projects using big analytics toolsets in a CI/CD environment. Analytics toolsets may include DS tools/Spark/Databricks, and other technologies offered by Microsoft Azure or open-source toolsets. This role will also help automate the end-to-end cycle with Machine Learning Services and Pipelines. Responsibilities Delivery of key Advanced Analytics/Data Science projects within time and budget, particularly around DevOps/MLOps and Machine Learning models in scope Collaborate with data engineers and ML engineers to understand data and models and leverage various advanced analytics capabilities Ensure on time and on budget delivery which satisfies project requirements, while adhering to enterprise architecture standards Use big data technologies to help process data and build scaled data pipelines (batch to real time) Automate the end-to-end ML lifecycle with Azure Machine Learning and Azure/AWS/GCP Pipelines. Setup cloud alerts, monitors, dashboards, and logging and troubleshoot machine learning infrastructure Automate ML models deployments Qualifications Minimum 3years of hands-on work experience in data science / Machine learning Minimum 3year of SQL experience Experience in DevOps and Machine Learning (ML) with hands-on experience with one or more cloud service providers. BE/BS in Computer Science, Math, Physics, or other technical fields. Data Science - Hands on experience and strong knowledge of building machine learning models - supervised and unsupervised models Programming Skills - Hands-on experience in statistical programming languages like Python and database query languages like SQL Statistics - Good applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators Any Cloud - Experience in Databricks and ADF is desirable Familiarity with Spark, Hive, Pig is an added advantage Model deployment experience will be a plus Experience with version control systems like GitHub and CI/CD tools Experience in Exploratory data Analysis Knowledge of ML Ops / DevOps and deploying ML models is required Experience using MLFlow, Kubeflow etc. will be preferred Experience executing and contributing to ML OPS automation infrastructure is good to have Exceptional analytical and problem-solving skills Show more Show less

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 a skilled Data Scientist with 2 to 5 years of experience, specializing in Machine Learning, PySpark, and Databricks, with a proven track record in long-range demand and sales forecasting. This role is crucial for the development and implementation of an automotive OEM s next-generation Intelligent Forecast Application. The position will involve building, optimizing, and deploying large-scale machine learning models for complex, long-term forecasting challenges using distributed computing frameworks, specifically PySpark on the Databricks platform. The work will directly support strategic decision-making across the automotive value chain, including areas like long-term demand planning, production scheduling, and inventory optimization.The ideal candidate will have hands-on experience developing and deploying ML models for forecasting, particularly long-range predictions, in a production environment using PySpark and Databricks. This role requires strong technical skills in machine learning, big data processing, and time series forecasting, combined with the ability to work effectively within a technical team to deliver robust and scalable long-range forecasting solutions. Role Description: Machine Learning Model Development & Implementation for Long-Range Forecasting: Design, develop, and implement scalable and accurate machine learning models specifically for long-range demand and sales forecasting challenges. Apply advanced time series analysis techniques and integrate them with machine learning models leveraging PySpark for data processing and model training on large datasets within the Databricks environment. Implement probabilistic forecasting methods using PySpark to capture uncertainty in long-range predictions. Develop robust solutions for hierarchical and grouped long-range forecasting on distributed data. Data Processing and Feature Engineering with PySpark: Build and optimize large-scale data pipelines for ingesting, cleaning, transforming, and engineering features relevant to long-range forecasting from diverse, complex automotive datasets using PySpark on Databricks. Deployment and MLOps on Databricks: Develop and implement robust code for model training, inference, and deployment of long-range forecasting models directly within the Databricks platform. Apply MLOps principles compatible with Databricks workflows for model versioning, monitoring, retraining, and managing the lifecycle of long-range ML forecasting models in production. Collaborate with Data Engineering and IT Operations to ensure seamless deployment and operational efficiency of the forecasting application on Databricks. Performance Evaluation & Optimization: Evaluate long-range forecasting model performance using relevant metrics (e.g., MAE, RMSE, MAPE, considering metrics suitable for longer horizons) and optimize models and data processing pipelines for improved accuracy and efficiency within the PySpark/Databricks ecosystem. Technical Collaboration: Work effectively as part of a technical team, collaborating with other data scientists, data engineers, and software developers to integrate ML long-range forecasting solutions into the broader forecasting application built on Databricks. Communicate technical details and forecasting results effectively within the technical team. Qualifications Education: Bachelors or Masters degree in Data Science, Computer Science, Statistics, Applied Mathematics, or a closely related quantitative field. Experience:2 to 5 years of hands-on experience in a Data Scientist or Machine Learning Engineer role. Proven experience developing and deploying machine learning models in a production environment. Demonstrated experience in long-range demand and sales forecasting. Significant hands-on experience with PySpark for large-scale data processing and machine learning. Extensive practical experience working with the Databricks platform, including notebooks, jobs, and ML capabilities. Expert proficiency in PySpark . Expert proficiency in the Databricks platform . Strong proficiency in Python and SQL. Experience with machine learning libraries compatible with PySpark (e.g., MLlib, or integrating other libraries). Experience with advanced time series forecasting techniques and their implementation. Experience with distributed computing concepts and optimization techniques relevant to PySpark. Hands-on experience with a major cloud provider (Azure, AWS, or GCP) in the context of using Databricks. Familiarity with MLOps concepts and tools used in a Databricks environment. Experience with data visualization tools. Analytical skills with a deep understanding of machine learning algorithms and their application to forecasting. Ability to troubleshoot and solve complex technical problems related to big data and machine learning workflows. Preferred Qualifications Experience with specific long-range forecasting methodologies and libraries used in a distributed environment. Experience with real-time or streaming data processing using PySpark for near-term forecasting components that might complement long-range models. Familiarity with automotive data types relevant to long-range forecasting (e.g., economic indicators affecting car sales, long-term market trends). Experience with distributed version control systems (e.g., Git). Knowledge of agile development methodologies. Soft Skills: Collaboration: Ability to work effectively as part of a technical team. Communication: Clear and concise communication of technical details and forecasting results. Problem-Solving: Ability to tackle complex technical challenges and find efficient solutions. Learning Agility: Eagerness to learn and adapt to new technologies and methodologies within the PySpark/Databricks ecosystem and advancements in long-range forecasting. Ability to understand business needs related to long-term planning. 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.

Position Overview We are looking for a skilled and driven Data Scientist with 5-7 years of experience in data Key Responsibilities  Analyze large and complex datasets to uncover meaningful patterns, trends, and insights.  Build, deploy, and validate predictive models, leveraging machine learning techniques and deep learning frameworks.  Develop and implement Generative AI models to solve business challenges.  Perform data preprocessing, exploratory data analysis (EDA), and feature engineering (FE) to prepare data for modeling and optimization.  Write efficient and scalable code in Python, R, and SQL to manage and manipulate data.  Collaborate with cross-functional teams to generate actionable insights that support business decision-making.  Create clear and impactful data visualizations to communicate findings to both technical and non-technical stakeholders.  Continuously monitor and improve model performance using optimization techniques.  Stay up-to-date with advancements in data science, machine learning, and AI technologies. Required Skills And Qualifications  Experience: 5-7 years of hands-on experience in data analysis, predictive modeling, and advanced analytics.  Technical Proficiency Strong programming skills in Python, R, and SQL. In-depth knowledge of machine learning techniques, deep learning frameworks, and Generative AI. Proficiency in data handling, pre-processing, exploratory data analysis (EDA), feature engineering (FE), and optimization techniques.  Analytical Skills Strong problem-solving abilities and attention to detail. Capability to derive actionable insights and present findings in a business friendly manner. Skills: optimization techniques,generative ai,sql,machine learning,gen ai,data preprocessing,feature engineering (fe),python,deep learning,exploratory data analysis (eda),r Show more Show less

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.

Overview We are PepsiCo PepsiCo is one of the world's leading food and beverage companies with more than $79 Billion in Net Revenue and a global portfolio of diverse and beloved brands. We have a complementary food and beverage portfolio that includes 22 brands that each generate more than $1 Billion in annual retail sales. PepsiCo's products are sold in more than 200 countries and territories around the world. PepsiCo's strength is its people. We are over 250,000 game changers, mountain movers and history makers, located around the world, and united by a shared set of values and goals. We believe that acting ethically and responsibly is not only the right thing to do, but also the right thing to do for our business. At PepsiCo, we aim to deliver top-tier financial performance over the long term by integrating sustainability into our business strategy, leaving a positive imprint on society and the environment. We call this Winning with Pep+ Positive . For more information on PepsiCo and the opportunities it holds, visit www.pepsico.com. PepsiCo Data Analytics & AI Overview: With data deeply embedded in our DNA, PepsiCo Data, Analytics and AI (DA&AI) transforms data into consumer delight. We build and organize business-ready data that allows PepsiCo’s leaders to solve their problems with the highest degree of confidence. Our platform of data products and services ensures data is activated at scale. This enables new revenue streams, deeper partner relationships, new consumer experiences, and innovation across the enterprise. The Data Science Pillar in DA&AI will be the organization where Data Scientist and ML Engineers report to in the broader D+A Organization. Also DS will lead, facilitate and collaborate on the larger DS community in PepsiCo. DS will provide the talent for the development and support of DS component and its life cycle within DA&AI Products. And will support “pre-engagement” activities as requested and validated by the prioritization framework of DA&AI. Data Scientist-Gurugram and Hyderabad The role will work in developing Machine Learning (ML) and Artificial Intelligence (AI) projects. Specific scope of this role is to develop ML solution in support of ML/AI projects using big analytics toolsets in a CI/CD environment. Analytics toolsets may include DS tools/Spark/Databricks, and other technologies offered by Microsoft Azure or open-source toolsets. This role will also help automate the end-to-end cycle with Machine Learning Services and Pipelines. Responsibilities Delivery of key Advanced Analytics/Data Science projects within time and budget, particularly around DevOps/MLOps and Machine Learning models in scope Collaborate with data engineers and ML engineers to understand data and models and leverage various advanced analytics capabilities Ensure on time and on budget delivery which satisfies project requirements, while adhering to enterprise architecture standards Use big data technologies to help process data and build scaled data pipelines (batch to real time) Automate the end-to-end ML lifecycle with Azure Machine Learning and Azure/AWS/GCP Pipelines. Setup cloud alerts, monitors, dashboards, and logging and troubleshoot machine learning infrastructure Automate ML models deployments Qualifications Minimum 3years of hands-on work experience in data science / Machine learning Minimum 3year of SQL experience Experience in DevOps and Machine Learning (ML) with hands-on experience with one or more cloud service providers BE/BS in Computer Science, Math, Physics, or other technical fields. Data Science - Hands on experience and strong knowledge of building machine learning models - supervised and unsupervised models Programming Skills - Hands-on experience in statistical programming languages like Python and database query languages like SQL Statistics - Good applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators Any Cloud - Experience in Databricks and ADF is desirable Familiarity with Spark, Hive, Pig is an added advantage Model deployment experience will be a plus Experience with version control systems like GitHub and CI/CD tools Experience in Exploratory data Analysis Knowledge of ML Ops / DevOps and deploying ML models is required Experience using MLFlow, Kubeflow etc. will be preferred Experience executing and contributing to ML OPS automation infrastructure is good to have Exceptional analytical and problem-solving skills Show more Show less

Overview We are PepsiCo PepsiCo is one of the world's leading food and beverage companies with more than $79 Billion in Net Revenue and a global portfolio of diverse and beloved brands. We have a complementary food and beverage portfolio that includes 22 brands that each generate more than $1 Billion in annual retail sales. PepsiCo's products are sold in more than 200 countries and territories around the world. PepsiCo's strength is its people. We are over 250,000 game changers, mountain movers and history makers, located around the world, and united by a shared set of values and goals. We believe that acting ethically and responsibly is not only the right thing to do, but also the right thing to do for our business. At PepsiCo, we aim to deliver top-tier financial performance over the long term by integrating sustainability into our business strategy, leaving a positive imprint on society and the environment. We call this Winning with Pep+ Positive . For more information on PepsiCo and the opportunities it holds, visit www.pepsico.com. PepsiCo Data Analytics & AI Overview: With data deeply embedded in our DNA, PepsiCo Data, Analytics and AI (DA&AI) transforms data into consumer delight. We build and organize business-ready data that allows PepsiCo’s leaders to solve their problems with the highest degree of confidence. Our platform of data products and services ensures data is activated at scale. This enables new revenue streams, deeper partner relationships, new consumer experiences, and innovation across the enterprise. The Data Science Pillar in DA&AI will be the organization where Data Scientist and ML Engineers report to in the broader D+A Organization. Also DS will lead, facilitate and collaborate on the larger DS community in PepsiCo. DS will provide the talent for the development and support of DS component and its life cycle within DA&AI Products. And will support “pre-engagement” activities as requested and validated by the prioritization framework of DA&AI. Data Scientist-Gurugram and Hyderabad The role will work in developing Machine Learning (ML) and Artificial Intelligence (AI) projects. Specific scope of this role is to develop ML solution in support of ML/AI projects using big analytics toolsets in a CI/CD environment. Analytics toolsets may include DS tools/Spark/Databricks, and other technologies offered by Microsoft Azure or open-source toolsets. This role will also help automate the end-to-end cycle with Machine Learning Services and Pipelines. Responsibilities Delivery of key Advanced Analytics/Data Science projects within time and budget, particularly around DevOps/MLOps and Machine Learning models in scope Collaborate with data engineers and ML engineers to understand data and models and leverage various advanced analytics capabilities Ensure on time and on budget delivery which satisfies project requirements, while adhering to enterprise architecture standards Use big data technologies to help process data and build scaled data pipelines (batch to real time) Automate the end-to-end ML lifecycle with Azure Machine Learning and Azure/AWS/GCP Pipelines. Setup cloud alerts, monitors, dashboards, and logging and troubleshoot machine learning infrastructure Automate ML models deployments Qualifications Minimum 3years of hands-on work experience in data science / Machine learning Minimum 3year of SQL experience Experience in DevOps and Machine Learning (ML) with hands-on experience with one or more cloud service providers. BE/BS in Computer Science, Math, Physics, or other technical fields. Data Science - Hands on experience and strong knowledge of building machine learning models - supervised and unsupervised models Programming Skills - Hands-on experience in statistical programming languages like Python and database query languages like SQL Statistics - Good applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators Any Cloud - Experience in Databricks and ADF is desirable Familiarity with Spark, Hive, Pig is an added advantage Model deployment experience will be a plus Experience with version control systems like GitHub and CI/CD tools Experience in Exploratory data Analysis Knowledge of ML Ops / DevOps and deploying ML models is required Experience using MLFlow, Kubeflow etc. will be preferred Experience executing and contributing to ML OPS automation infrastructure is good to have Exceptional analytical and problem-solving skills Show more Show less

Key Responsibilities Provide strategic direction and technical leadership in applying machine learning/ AI within the organization, aligned with company goals and challenges. Influence R&D roadmap based on deep industry understanding and business needs. Drive innovation with novel solutions in the automotive industry, advancing applied science through latest research implementation. Act as a key liaison between R&D and stakeholders, ensuring technical alignment with business objectives. Collaborate with cross-functional teams to define and integrate machine learning solutions into products and services. Publish research in top conferences, advancing field knowledge. Provide mentorship to applied scientists, ensuring project success.Contribute to team growth through training and collaborative projects. Define metrics for model evaluation in offline and real-time settings. Quantify and communicate ML impact to executives, influencing decisions. Champion ethical AI practices, ensuring compliance and standards. Define best practices for ML model development, testing, validation, and monitoring. Demonstrate data-driven business acumen and technical expertise across development lifecycle. Maintain technical excellence in algorithm development and deployment. Required Qualifications Bachelors/ Masters / PhD in Data Science, Mathematics, Operations Research or related field. The right candidate has a background in probability and statistics, applied mathematics, or computational science and a history of solving difficult problems with good business impact using a scientific approach. Experience working with major ML algorithms and frameworks. Significant experience in Data Science and Machine Learning with a strong proven track record in delivering positive business impact. 11+ years of hands-on experience with analytics, data science and big data experience in a business context. Proficient in Machine Learning, Deep Learning and Explainable AI concepts with the ability to explain them clearly to non-technical audiences Experienced in developing and adapting state-of-the-art models for specific business needs, demonstrating a track record of success in complex, cross-functional projects. Utilizing Probability and Statistics to derive meaningful insights from data. Proficient in deep learning frameworks like TensorFlow, Keras, and PyTorch, and utilizing computer vision (OpenCV) and natural language processing (spaCy) libraries. Implementing deep learning techniques, particularly Neural Networks. Applying both fundamental and advanced Natural Language Processing concepts. Implementing Computer Vision methodologies. Experience with Large Language Models is a must. Should have fine-tuned LLM on domain dataset. Utilizing Git for efficient code management and version control. Leveraging cloud platforms such as Google Cloud Platform, Microsoft Azure, and Amazon Web Services. Should be well versed with Design of Experiments. Experience in programming with Python and SQL or similar languages. Experience in distributed model training. Proficient in data visualization and presentation, transforming complex analyses into actionable insights. Experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, Airflow). Experience using ML platforms such as Databricks and SageMaker. Preferred Qualifications Experience in Recommendation Engine, Natural Language Processing, Computer Vision Domain knowledge of automotive industry. Enjoys discovering and solving problems; proactively seeking clarification of requirements and direction; being a self-starter who takes responsibility when required. Familiarity with Spark Strong interpersonal, verbal, visual and presentation skills, ability to communicate complex findings in a simple manner to executives. Ability to work collaboratively across multiple products and application teams. A willingness to learn, share and improve.

•5-10 years of industry experience in developing Agentic AI solutions including machine learning. •Strong skills in Python along with experience with relevant libraries. •Experience in working with large datasets, implementing scalable AI solutions.

Notice Period : Candidates with up to 1-month notice preferred Findability Sciences is looking for a hands-on AI expert with experience in developing and implementing comprehensive predictive and generative AI solutions. This is a core technical role rather than a support-based one. You will lead innovation and delivery across multiple industries. What we are looking for: 3+ years of experience in AI/ML with a Masters or Ph.D. in a relevant field Strong knowledge of Python, SQL, Machine Learning, LLMs, RAG, and Prompt Engineering Experience in predictive modeling, time series forecasting, and generative AI use cases Experience with ML Ops workflows model deployment, monitoring, versioning, and pipeline automation Key Responsibilities: Design and deploy predictive & GenAI solutions for real-world business use cases Build and optimize LLM-based workflows for summarization, retrieval, and Q&A Collaborate with cross-functional teams to define use cases and develop proof-of-concepts Translate business problems into ML/GenAI solutions and deliver outcomes Validate, compare, and optimize models for performance and scalability Apply basic ML Ops practices to ensure smooth deployment and monitoring of models Contribute to reusable pipelines, prompt tuning strategies, and production integration Present actionable insights and models to stakeholders clearly and effectively

Notice Period: Candidates with up to 1-month notice preferred Findability Sciences is looking for a hands-on AI expert with experience in developing and implementing comprehensive predictive and generative AI solutions. This is a core technical role rather than a support-based one. What we are looking for: 3+ years of experience in AI/ML with a Masters or Ph.D. in a relevant field Strong knowledge of Python, SQL, Machine Learning, LLMs, RAG, and Prompt Engineering Experience in predictive modeling, time series forecasting, and generative AI use cases Experience with ML Ops workflows model deployment, monitoring, versioning, and pipeline automation Key Responsibilities: Design and deploy predictive & GenAI solutions for real-world business use cases Build and optimize LLM-based workflows for summarization, retrieval, and Q&A Collaborate with cross-functional teams to define use cases and develop proof-of-concepts Translate business problems into ML/GenAI solutions and deliver outcomes Validate, compare, and optimize models for performance and scalability Apply basic ML Ops practices to ensure smooth deployment and monitoring of models Contribute to reusable pipelines, prompt tuning strategies, and production integration Present actionable insights and models to stakeholders clearly and effectively

Bachelor's/Master's degree in computer science (or similar degrees) 2-4 years of experience as a Data Scienti st in a fast-paced organizati on, preferably B2C Familiarity with Neural Networks, Machine Learning, etc Familiarity with tools like SQL, R, Python, etc. Strong understanding of Stati sti cs and Linear Algebra Strong understanding of hypothesis/model testi ng and ability to identi fy common model testi ng errors Experience designing and running A/B tests and drawing insights from them Proficiency in machine learning algorithms Excellent analyti cal skills to fetch data from reliable sources to generate accurate insights Experience in tech and product teams is a plus Bonus points for: -Experience in working on personalizati on or other ML problems -Familiarity with Big Data tech stacks like Apache Spark, Hadoop, Redshif if interested please contact at deblina.s@wengerwatson.com

So, what s the role all about NICE provides state-of-the-art enterprise level AI and analytics for all forms of business communications between speech and digital. We are a world class research team developing new algorithms and approaches to help companies with solving critical issues such as identifying their best performing agents, preventing fraud, categorizing customer issues, and determining overall customer satisfaction. If you have interacted with a major contact center in the last decade, it is very likely we have processed your call. The research group partners with all areas of NICE s business to scale out the delivery of new technology and AI models to customers around the world that are tailored to their company, industry, and language needs. How will you make an impact Conduct cutting-edge research and develop advanced NLP algorithms and models. Build and fine-tune deep learning and machine learning models, with a focus on large language models. Work closely with internal stakeholders to define model requirements and ensure alignment with business objectives. Develop AI predictive models and perform data and model accuracy analyses. Produce and present findings, technical concepts, and model recommendations to both technical and non-technical stakeholders. Develop and maintain scripts/tools to automate both new model production and updates to existing model packages. Stay abreast of the latest advancements in data science research and contribute to the development of our knowledge base. Collaborate with developers to design automation and tool improvements for model building. Maintain documentation of processes and projects across all supported languages and environments. Have you got what it takes Masters degree in the field of Computer Science, Technology, Engineering, Math, or equivalent practical experience Minimum of 4-7 years of data science work experience, including implementing machine learning and NLP models using real-life data. Strong research skills, with a proven track record of developing and implementing advanced machine learning algorithms. Advanced knowledge of supervised and unsupervised machine learning algorithms. Excellent proficiency in Python programming. Experience with deep learning models and libraries such as PyTorch, TensorFlow, Hands-on experience with transformer models and Gen AI frameworks (HuggingFace, AWS Bedrock, Azure Foundry ). Strong verbal and written communication skills, including effective presentation abilities. Ability to work independently and as part of a team, demonstrating analytical thinking and problem-solving skills. You will have an advantage if you also have: Experience deploying models in production environments, ensuring scalability and performance using AWS/GCP/Azure Familiarity with relational databases and query languages (e. g. , MSSQL) and basic SQL knowledge. Experience with Retrieval-Augmented Generation (RAG) pipelines Understanding of multimodal models (text, audio, vision). Experience in Customer Experience domains. Experience working on international, globe-spanning teams. Past participation in a formal research setting. Experience as part of a software organization What s in it for you Enjoy NICE-FLEX! Requisition ID : 7294 Reporting into : Tech Manager Role Type : Individual Contributor About NICE

Coditas Solutions is seeking a highly skilled and motivated Data Scientist to join our dynamic team. As a Data Scientist, you will play a key role in designing, implementing, and optimizing machine learning models and algorithms to solve complex business challenges. If you have a passion for leveraging AI and ML technologies to drive innovation, this is an exciting opportunity to contribute to groundbreaking projects. Roles and Responsibilities Design, implement, and optimize machine learning algorithms using R and Python. Work on developing predictive models and decision-making systems. Conduct exploratory data analysis to understand data patterns and insights. Collaborate with data engineers to ensure the availability and quality of data for model training. Deploy machine learning models into production environments. Collaborate with cross-functional teams to integrate models into existing systems. Continuously optimize and improve the performance of machine learning models. Stay updated on the latest advancements in ML algorithms and technologies. Work closely with software engineers to ensure seamless integration of AI/ML solutions. Collaborate with clients to understand their business requirements and customize solutions accordingly. Technical Skills Excellent programming skills with the ability to implement complex algorithms in Python or R. Experience with cloud-based platforms (AWS, Azure, GCP) for deploying machine learning models. Strong experience of minimum 3 years in developing and implementing machine learning algorithms. Experience with model deployment and integration into production systems. Hands-on experience with use of standard classical machine learning libraries such as Scikit learn, NLTK, OpenCV as well as deep learning libraries Tensorflow, PyTorch, Keras. Understanding of machine learning algorithms, techniques, and concepts (linear regression, logistic regression, decision trees, random forests, neural networks, etc.). Experience with data preprocessing, feature engineering, and model evaluation techniques of structured and unstructured data. Proven experience with identifying, creating and selecting relevant features or variables to enhance model performance. Ability to collaborate effectively with cross-functional teams. Previous experience working on real-world AI/ML projects. Should be focused on linear algebra, machine learning, and statistics & probability are preferred. Ability to have a basic knowledge of the LLMs and optimal use of the GenAI models. Strong problem-solving and critical-thinking skills. Excellent communication and collaboration skills. Join our team and be part of a fast-paced and innovative work environment where your expertise will make a significant impact on our organization's growth and success.

AlgoAnalytics is looking for a Data Scientist who has the passion for AI/ML solution development and cutting-edge work in Gen AI. Minimum 3 years of experience in AI/MI including Deep Learning, and exposure to Gen AI/LLM is required along with tools/techs/libraries like Agentic AI, Dify/Autogen, Langraphetc. Detailed understanding of Cloud technologies and Deployment with good hands-on experience is an added advantage. Key Responsibilities: AI/ML & Gen AI Development: Develop Gen AI/LLM models using OpenAI, Llama, or other open-source/offline models. Utilize Agentic AI frameworks and tools like Dify, Autogen, Langraph to build intelligent systems. Perform prompt engineering, fine-tuning, and dataset-specific model optimization. Implement cutting-edge research from AI/ML and Gen AI domains. Work on cloud-based deployments of AI/ML models for scalability and production readiness. Leadership & Mentoring: Troubleshoot AI/ML, Gen AI, and cloud deployment challenges. Mentor junior team members and contribute to their skill development. Identify team members for mentoring, hiring, and technical initiatives. Ensure smooth project execution, deadline management, and client interactions. Research & Innovation: Explore and implement recent AI/ML research in practical applications. Contribute to research publications, internal knowledge-sharing, and AI innovation. Qualifications: 2-3 years of hands-on experience in Machine Learning, Deep Learning, and Gen AI/LLMs. Bachelors/Masters degree in Computer Science, Engineering, Mathematics, or Statistics (with strong programming knowledge). Skills: Programming: Strong expertise in Python and relevant ML/AI libraries. AI/ML & Gen AI: Deep understanding of Machine Learning, Deep Learning, and Generative AI/LLMs. Agentic AI & Automation: Experience with Dify, Autogen, Langraph, and similar tools. Cloud & Deployment: Knowledge of cloud platforms (AWS, GCP, Azure) and MLOps deployment pipelines. Communication & Leadership: Strong ability to manage teams, meet project deadlines, and collaborate with clients. Note - We would prefer only Pune based candidates

Job Overview We are looking for a dynamic and innovative Full Stack Data Scientist with 45 years of experience who excels in end-to-end data science solutions. The ideal candidate is a tech-savvy professional passionate about leveraging data to solve complex problems, develop predictive models, and drive business impact in the MarTech domain. Key Responsibilities 1. Data Engineering & Preprocessing Collect, clean, and preprocess structured and unstructured data from various sources. Perform advanced feature engineering, outlier detection, and data transformation. Collaborate with data engineers to ensure seamless data pipeline development. 2. Machine Learning Model Development Design, train, and validate machine learning models (supervised, unsupervised, deep learning). Optimize models for business KPIs such as accuracy, recall, and precision. Innovate with advanced algorithms tailored to marketing technologies. 3. Full Stack Development Build production-grade APIs for model deployment using frameworks like Flask, FastAPI, or Django. Develop scalable and modular code for data processing and ML integration. 4. Deployment & Operationalization Deploy models on cloud platforms (AWS, Azure, or GCP) using tools like Docker and Kubernetes. Implement continuous monitoring, logging, and retraining strategies for deployed models. 5. Insight Visualization & Communication Create visually compelling dashboards and reports using Tableau, Power BI, or similar tools. Present insights and actionable recommendations to stakeholders effectively. 6. Collaboration & Teamwork Work closely with marketing analysts, product managers, and engineering teams to solve business challenges. Foster a collaborative environment that encourages innovation and shared learning. 7. Continuous Learning & Innovation Stay updated on the latest trends in AI/ML, especially in marketing automation and analytics. Identify new opportunities for leveraging data science in MarTech solutions. Qualifications Educational Background Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field. Technical Skills Programming Languages: Python (must-have), R, or Julia; familiarity with Java or C++ is a plus. ML Frameworks: TensorFlow, PyTorch, Scikit-learn, or XGBoost. Big Data Tools: Spark, Hadoop, or Kafka. Cloud Platforms: AWS, Azure, or GCP for model deployment and data pipelines. Databases: Expertise in SQL and NoSQL (e.g., MongoDB, Cassandra). Visualization: Mastery of Tableau, Power BI, Plotly, or D3.js. Version Control: Proficiency with Git for collaborative coding. Experience 4–5 years of hands-on experience in data science, machine learning, and software engineering. Proven expertise in deploying machine learning models in production environments. Experience in handling large datasets and implementing big data technologies. Soft Skills Strong problem-solving and analytical thinking. Excellent communication and storytelling skills for technical and non-technical audiences. Ability to work collaboratively in diverse and cross-functional teams. Preferred Qualifications Experience with Natural Language Processing (NLP) and Computer Vision (CV). Familiarity with CI/CD pipelines and DevOps for ML workflows. Exposure to Agile project management methodologies. Role & responsibilities Preferred candidate profile

Designation - Sr.Executive (Data Scientist) Location - Wadgaonsheri, Pune Experience - 2 to 3 yrs Type - On site, Full Time Responsibilities Analyze large volumes of structured and unstructured data to extract meaningful insights. Build and deploy predictive models using machine learning algorithms. Design and execute A/B tests and experiments to validate hypotheses. Collaborate with business stakeholders to identify opportunities where data science can add value. Visualize data and communicate findings through dashboards and presentations. Work with data engineering teams to improve data availability, integrity, and pipelines. Requirements and skills Proven experience as a Data Scientist Understanding and experience of machine-learning, NLP. Knowledge of R, SQL and Python. Experience using business intelligence tool (e.g . Power bi ) would be an advantage and operations research Analytical mind and business acumen Strong math skills (e.g. Statistics, Algebra, Probability ) Problem-solving aptitude Key Skills Machine Learning, Python, SQL, NLP, Deployment. Benefits 1. Exposure in fastest growing industry 2. Experience in domain and support 3. In depth experience in Data Analysis If interested, kindly share your resume to bshinde@mdindia.com with below required details- Total Experience- Relevant Experience- Current CTC- Expected CTC- Current Company- Current Designation- Reason for leaving- Are you serving Notice period- If no, then Notice Period- How soon you can join, if selected- Current Location in Pune- Available for face to face interview- How many years of experience do you have in ML?- How many years of experience do you have in Python?- How many years of experience do you have in SQL?- Updated CV-