Data Science Manager

Job Title: Senior Manager, Data Science Level: Senior Manager (Individual Contributor + People Management) Onsite / Hybrid : Contract- Can be little flexible but We Bi/ Weekly Presence will be highly preferred Experience: 12–15 years in data science, machine learning, or quantitative analytics Team: Leads a team of 3–6 data scientists across US and offshore Domain: Supply Chain, Demand Forecasting, Operations Analytics About the Role We are looking for a Senior Manager of Data Science to lead the end-to-end design, development, and deployment of advanced machine learning solutions for supply chain demand forecasting and root cause analysis. This is a hands-on leadership role — you will architect the analytical framework, guide a cross-functional team of data scientists, and serve as the primary technical interface with senior stakeholders. Required Qualifications • Experience: 12–15 years of progressive experience in data science, machine learning, or quantitative analytics — with at least 4 years in a lead or management role • Proven track record delivering end-to-end ML pipelines in production environments — from raw data through model deployment and monitoring • Hands-on experience with demand forecasting, supply chain analytics, or operations research in an industrial, manufacturing, or distribution context • Demonstrated experience leading cross-functional analytics teams, including offshore or distributed team members • Experience presenting complex analytical findings to C-level and VP-level stakeholders with measurable business impact Technical Skills • Expert-level proficiency in Python: scikit-learn, XGBoost, LightGBM, statsmodels, pandas, numpy, scipy • Deep expertise in ensemble methods — gradient boosting (GBM, XGBoost, LightGBM) and random forest variants including quantile regression forests • Proficiency in probabilistic forecasting: quantile regression, prediction interval construction and calibration, Winkler scoring, pinball loss • Strong statistical foundation: hypothesis testing, KS tests, distribution shift detection, time-series analysis (ACF, PACF, change-point detection) • Experience with feature engineering for time-series and supply chain data: lag features, rolling statistics, Fourier encoding, interaction terms • Proficiency with experiment tracking and MLOps tooling (MLflow, DVC, or equivalent); familiarity with CI/CD for ML pipelines • Ability to write and review production-quality Python code; experience with SQL for data extraction and transformation • Familiarity with cloud platforms (AWS, Azure, or GCP) for model training, deployment, and scheduled execution Education • Master's degree or PhD in Data Science, Statistics, Machine Learning, Operations Research, Industrial Engineering, or a related quantitative field • Bachelor's degree in a quantitative discipline with equivalent industry experience considered