Senior Manager, Data Science

Job Title Senior Manager, Data Science Level Senior Manager Experience 12–15 years in data science, machine learning, or quantitative 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 Preferred Qualifications • Experience with intermittent demand modelling: Croston method, Syntetos-Boylan Approximation (SBA), ADI-CV² classification • Familiarity with Forecast Value Add (FVA) frameworks and multi-layer forecast override governance • Experience with inflection point detection and structural break analysis in demand time series • Background in consulting or professional services — experience running client-facing analytical engagements with gate reviews and formal deliverables • Familiarity with S&OP (Sales and Operations Planning) processes and integrated business planning cycles • Experience deploying analytical tools in enterprise environments with IT security and compliance constraints • Publications, conference presentations, or open-source contributions in ML, forecasting, or supply chain analytics People & Delivery Leadership • Lead, mentor, and develop a team of 3–6 data scientists across US and India; provide technical guidance, career development support, and performance feedback • Define team roadmap, prioritise work streams, and ensure on-time delivery against project milestones — including intermediate client-facing outputs at each analytical phase • Collaborate closely with the India-based Lead Data Scientist to ensure seamless coordination across time zones; act as the primary technical escalation point for the offshore team • Conduct design validation workshops, cluster review sessions, and gate reviews with business stakeholders at each phase of the analytical cycle • Translate complex ML findings into business-language reports and executive presentations,