131 Statsmodels Jobs - Page 6

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. Show more Show less

Dreaming big is in our DNA Its who we are as a company Its our culture Its our heritage And more than ever, its our future A future where were always looking forward Always serving up new ways to meet lifes moments A future where we keep dreaming bigger We look for people with passion, talent, and curiosity, and provide them with the teammates, resources and opportunities to unleash their full potential The power we create together when we combine your strengths with ours is unstoppable Are you ready to join a team that dreams as big as you do 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 ML Engineer Location: Bangalore Reporting to: Director Data Analytics Purpose of the role Anheuser-Busch InBev (AB InBev)s Supply Analytics is responsible for building competitive differentiated solutions that enhance brewery efficiency through data-driven insights We optimize processes, reduce waste, and improve productivity by leveraging advanced analytics and AI-driven solutions, Senior MLE, will be responsible for the end-to-end deployment of machine learning models on edge devices You will take ownership of all aspects of edge deployment, including model optimization, scaling complexities, containerization, and infrastructure management, ensuring high availability and performance, Key tasks & accountabilities Lead the entire edge deployment lifecycle, from model training to deployment and monitoring on edge devices Develop, and maintain a scalable Edge ML pipeline that enables real-time analytics at brewery sites, Optimize and containerize models using Portainer, Docker, and Azure Container Registry (ACR) to ensure efficient execution in constrained edge environments, Own and manage the GitHub repository, ensuring structured, well-documented, and modularized code for seamless deployments, Establish robust CI/CD pipelines for continuous integration and deployment of models and services, Implement logging, monitoring, and alerting for deployed models to ensure reliability and quick failure recovery Ensure compliance with security and governance best practices for data and model deployment in edge environments, Document the thought process & create artifacts on team repo/wiki that can be used to share with business & engineering for sign off, Review code quality and design developed by the peers, Significantly improve the performance & reliability of our code that creates high quality & reproducible results, Develop internal tools/utils that improve productivity of entire team, Collaborate with other team members to advance the teams ability to ship high quality code fast! Mentor/coach junior team members to continuously upskill them, Maintain basic developer hygiene that includes but not limited to, writing tests, using loggers, readme to name a few, Qualifications, Experience, Skills Level of educational attainment required (1 or more of the following) Academic degree in, but not limited to, Bachelors or master's in computer application, Computer science, or any engineering discipline, Previous Work Experience 5+ years of real-world experience to develop scalable & high-quality ML models, Strong problem-solving skills with an owners mindset?proactively identifying and resolving bottlenecks, Technical Skills Required Proficiency with pandas, NumPy, SciPy, scikit-learn, stats models, TensorFlow, Good understanding of statistical computing, parallel processing, Experience with advanced TensorFlow distributed, NumPy, joblib, Good understanding of memory management & parallel processing in python, Profiling & optimization of production code, Strong at Python coding Exposure to working in IDEs such as VSC or PyCharm, Experience in code versioning using Git, maintaining modularized code base for multiple deployments, Experience in working in an Agile environment, In depth understand of data bricks (Workflows, cluster creation, repo management), In depth understanding of machine learning solution in Azure cloud, Best practices in coding standards, unit testing, and automation, Proficiency in Docker, Kubernetes, Portainer, and container orchestration for edge computing, Other Skills Required Experience in real-time analytics and edge AI deployments Exposure to DevOps practices, including infrastructure automation and monitoring tools Contributions to OSS or Stack overflow, And above all of this, an undying love for beer! We dream big to create future with more cheers

Position : AI / ML Engineer Job Type : Full-Time Location : Gurgaon, Haryana, India Experience : 2 Years Industry : Information Technology Domain : Demand Forecasting in Retail/Manufacturing Job Summary We are seeking a skilled Time Series Forecasting Engineer to enhance existing Python microservices into a modular, scalable forecasting engine. The ideal candidate will have a strong statistical background, expertise in handling multi-seasonal and intermittent data, and a passion for model interpretability and real-time insights. Key Responsibilities Develop and integrate advanced time-series models: MSTL, Croston, TSB, Box-Cox. Implement rolling-origin cross-validation and hyperparameter tuning. Blend models such as ARIMA, Prophet, and XGBoost for improved accuracy. Generate SHAP-based driver insights and deliver them to a React dashboard via GraphQL. Monitor forecast performance with Prometheus and Grafana; trigger alerts based on degradation. Core Technical Skills Languages : Python (pandas, statsmodels, scikit-learn) Time Series : ARIMA, MSTL, Croston, Prophet, TSB Tools : Docker, REST API, GraphQL, Git-flow, Unit Testing Database : PostgreSQL Monitoring : Prometheus, Grafana Nice-to-Have : MLflow, ONNX, TensorFlow Probability Soft Skills Strong communication and collaboration skills Ability to explain statistical models in layman terms Proactive problem-solving attitude Comfort working cross-functionally in iterative development environments Job Type: Full-time Pay: ₹400,000.00 - ₹800,000.00 per year Application Question(s): Do you have at least 2 years of hands-on experience in Python-based time series forecasting? Have you worked in retail or manufacturing domains where demand forecasting was a core responsibility? Are you currently authorized to work in India without sponsorship? Have you implemented or used ARIMA, Prophet, or MSTL in any of your projects? Have you used Croston or TSB models for forecasting intermittent demand? Are you familiar with SHAP for model interpretability? Have you containerized a forecasting pipeline using Docker and exposed it through a REST or GraphQL API? Have you used Prometheus and Grafana to monitor model performance in production? Work Location: In person Application Deadline: 05/06/2025 Expected Start Date: 05/06/2025

Machine Learning Engineer – Develop & optimize AI models for stock market prediction. Proficiency in Python, PyTorch, TensorFlow, scikit-learn, XGBoost, LightGBM, CatBoost, NumPy, statsmodels

Machine Learning Engineer – Develop & optimize AI models for stock market prediction. Proficiency in Python, PyTorch, TensorFlow, scikit-learn, XGBoost, LightGBM, CatBoost, NumPy, statsmodels

Data Scientist - GenAI Applications Position Overview We are seeking an experienced Data Scientist with 6+ years of experience to join our GenAI Applications team. This role leverages core data science expertisestatistical modeling, machine learning, and data analysis—to enhance and optimize generative AI solutions. The ideal candidate will bring strong foundational data science skills and apply them to the emerging field of generative AI. Key Responsibilities Statistical Analysis & Machine Learning Conduct statistical analysis and hypothesis testing to validate model performance and business impact Build and optimize traditional machine learning models (regression, classification, clustering, time series, etc.) Perform feature engineering, selection, and dimensionality reduction techniques Design and execute A/B tests and controlled experiments to measure model effectiveness Develop predictive models and recommendation systems to support business decision-making Data Analysis & Insights Analyze large, complex datasets to extract actionable insights and identify patterns Create comprehensive data visualizations and dashboards to communicate findings to stakeholders Perform exploratory data analysis (EDA) to understand data distributions, correlations, and anomalies Conduct cohort analysis, funnel analysis, and other business intelligence techniques Develop KPIs and metrics frameworks to measure success of AI initiatives GenAI Model Development & Implementation Apply data science methodologies to improve generative AI model performance and reliability Design experiments to evaluate and compare different generative AI approaches Implement fine-tuning strategies for pre-trained models using statistical optimization techniques Develop RAG (Retrieval-Augmented Generation) systems with focus on data quality and retrieval accuracy Create evaluation frameworks and metrics specifically for generative AI applications Research & Innovation Stay current with latest developments in generative AI, including emerging architectures and techniques Experiment with novel approaches to improve model performance, efficiency, and safety Collaborate with research teams to translate cutting-edge research into practical applications Evaluate and benchmark different model architectures and frameworks Cross-functional Collaboration Partner with product managers to translate business requirements into technical solutions Work closely with MLOps engineers to ensure scalable deployment and monitoring Collaborate with software engineers to integrate AI models into production applications Communicate complex technical concepts to stakeholders across various technical backgrounds Required Qualifications Technical Skills Bachelors, Master or Ph.D in Computer Science, Data Science, Statistics, Mathematics, or related field 6+ years of experience in data science with strong foundation in statistical methods and machine learning Expert proficiency in Python/R and data science libraries (pandas, numpy, scikit-learn, statsmodels, matplotlib, seaborn) Strong knowledge of statistical modeling, regression analysis, and experimental design Experience with SQL and database management for large-scale data analysis Proficiency in data visualization tools (Tableau, Power BI, or similar) Knowledge of machine learning frameworks (PyTorch, TensorFlow, XGBoost, LightGBM) Experience with cloud platforms (AWS, GCP, Azure) and distributed computing MLOps proficiency with tools like MLflow, Kubeflow, Docker, and CI/CD pipelines Familiarity with cloud ML platforms such as AWS SageMaker, Google Vertex AI, or Azure ML Strong understanding of data preprocessing, feature engineering, and model validation techniques GenAI & Advanced ML Hands-on experience applying data science techniques to natural language processing tasks Knowledge of deep learning architectures, particularly transformer models Experience with model evaluation metrics and statistical significance testing Understanding of bias detection, model interpretability, and responsible AI practices Familiarity with large language models and generative AI evaluation methodologies Soft Skills Strong analytical and problem-solving abilities Excellent communication skills with ability to explain complex technical concepts Experience working in agile development environments Self-motivated with ability to work independently and in team settings Preferred Qualifications Experience with advanced statistical methods (Bayesian analysis, causal inference, time series forecasting) Knowledge of optimization algorithms and mathematical programming Experience with multivariate testing and experimental design Background in natural language processing or computer vision from a data science perspective Experience with model compression and efficiency optimization techniques Experience leading data science projects and mentoring junior data scientists Data engineering experience including pipeline development, data preprocessing, and model deployment workflows