Senior Data Scientist

Role Overview: As a Senior Data Scientist, you will play a critical role in optimizing supply chain operations and maximizing inventory financial performance. Your focus will be on inventory and pricing optimization, while also working across diverse domains including forecasting, operational modeling, and product strategy. You will bring a rigorous, machine learning-driven mindset and thrive in cross-functional environments. Key Responsibilities: - Develop, deploy, and maintain predictive models for supply chain and inventory initiatives. - Implement regression, classification, clustering, and segmentation models to drive improvements in forecasting accuracy and operational decision-making. - Design and refine optimization models for inventory allocation, network design, sourcing strategies, and internal transfers using discrete optimization techniques. - Build and deploy time series models for demand forecasting, product performance tracking, and lifecycle modeling using classical methods and ML-based approaches. - Conduct in-depth EDA and statistical analysis, develop robust feature engineering pipelines, and identify key performance drivers to improve model robustness. - Collaborate with engineering, product, and operations teams to frame business problems, translate complex modeling outputs into actionable insights, and communicate findings effectively to stakeholders. - Build scalable data pipelines and decision-support systems using Python, Spark, and cloud-based platforms while ensuring production-grade deployment and monitoring of models. Qualification Required: - Bachelors or Masters degree in Data Science, Computer Science, Statistics, Operations Research, or related field. - 5+ years of experience building and deploying machine learning models in production environments. - Strong proficiency in Python (Pandas, NumPy, Scikit-learn), SQL, and Spark or other distributed computing frameworks. - Expertise in supervised and unsupervised learning, model evaluation, time series forecasting, discrete optimization, and simulation modeling. - Familiarity with data visualization tools and experience in tradeoff analysis. Company Details: N/A Role Overview: As a Senior Data Scientist, you will play a critical role in optimizing supply chain operations and maximizing inventory financial performance. Your focus will be on inventory and pricing optimization, while also working across diverse domains including forecasting, operational modeling, and product strategy. You will bring a rigorous, machine learning-driven mindset and thrive in cross-functional environments. Key Responsibilities: - Develop, deploy, and maintain predictive models for supply chain and inventory initiatives. - Implement regression, classification, clustering, and segmentation models to drive improvements in forecasting accuracy and operational decision-making. - Design and refine optimization models for inventory allocation, network design, sourcing strategies, and internal transfers using discrete optimization techniques. - Build and deploy time series models for demand forecasting, product performance tracking, and lifecycle modeling using classical methods and ML-based approaches. - Conduct in-depth EDA and statistical analysis, develop robust feature engineering pipelines, and identify key performance drivers to improve model robustness. - Collaborate with engineering, product, and operations teams to frame business problems, translate complex modeling outputs into actionable insights, and communicate findings effectively to stakeholders. - Build scalable data pipelines and decision-support systems using Python, Spark, and cloud-based platforms while ensuring production-grade deployment and monitoring of models. Qualification Required: - Bachelors or Masters degree in Data Science, Computer Science, Statistics, Operations Research, or related field. - 5+ years of experience building and deploying machine learning models in production environments. - Strong proficiency in Python (Pandas, NumPy, Scikit-learn), SQL, and Spark or other distributed computing frameworks. - Expertise in supervised and unsupervised learning, model evaluation, time series forecasting, discrete optimization, and simulation modeling. - Familiarity with data visualization tools and experience in tradeoff analysis. Company Details: N/A