Data Science Manager

Job Description Role description We are seeking a highly skilled Data Science Manager / Lead Data Scientist with strong hands-on expertise in SQL, Python, classical machine learning, forecasting, and causal inference. The ideal candidate will lead analytical projects, guide junior team members, and build end-to-end data science solutions that drive measurable business impact. This role requires a combination of technical excellence, strong problem-solving skills, and stakeholder leadership, with an ability to work across cross-functional teams. Key Responsibilities Technical Leadership & Delivery - Lead the design and development of machine learning models, forecasting solutions, causal inference approaches, and price elasticity models. - Build scalable analytical pipelines using Python (pandas, scikit-learn, statsmodels, PySpark optional) and advanced SQL. - Conduct exploratory data analysis (EDA), feature engineering, model development, validation, and deployment. - Oversee the end-to-end lifecycle of DS projectsfrom problem scoping to delivery. - Ensure model interpretability, robustness, reproducibility, and performance monitoring. Advanced Analytics & Methodology - Develop forecasting solutions using methods such as: - ARIMA, SARIMA, ETS, Prophet, LSTM (optional) - Hierarchical/ensemble forecasting - Build causal inference and impact-measurement models using approaches like: - Difference-in-differences, synthetic control, uplift modeling, regression discontinuity, causal trees - Analyze price elasticity, promotional uplift, or demand sensitivity using econometric and ML methods. - Design experiments (A/B tests) and statistical testing frameworks. Data Engineering & Tools - Write optimized SQL for large-scale datasets across warehouses (Snowflake, BigQuery, Redshift, etc.). - Build robust data pipelines and collaborate with data engineering teams for productionization. - Use version control (Git), CI/CD, and cloud environments such as AWS/Azure/GCP. Stakeholder Management & Business Partnership - Understand business problems across product, marketing, operations, commercial, or supply chain domains. - Translate ambiguous problems into measurable data science solutions. - Present insights, models, and recommendations to leadership in a clear, actionable manner. - Manage expectations, timelines, and delivery quality for multiple stakeholders. Team Leadership & Mentoring - Guide, mentor, and upskill junior and mid-level data scientists. - Conduct code reviews, enforce best practices, and maintain technical excellence across the team. - Foster a culture of innovation, testing, and continuous improvement. Required Qualifications - 810 years of experience in Data Science, Machine Learning, or Advanced Analytics. - Strong hands-on expertise in Python (pandas, NumPy, scikit-learn, statsmodels). - Expert-level SQL skills, with experience handling large, complex datasets. - Deep knowledge of classical ML algorithms such as regression, decision trees, ensemble models, clustering, and optimization. - Experience with forecasting, causal inference, or price elasticity modeling. - Proven experience delivering end-to-end data science projects independently. - Ability to explain complex analyses and model results to non-technical stakeholders. Preferred Skills - Experience with cloud platforms (AWS, Azure, GCP). - Familiarity with MLflow, Databricks, or data pipelines (Airflow, Prefect, DBT). - Exposure to A/B testing, experimental design, or uplift modeling. - Knowledge of visualization tools (Tableau, Power BI, Plotly). - Domain experience in Retail, CPG, Finance, E-commerce, or Supply Chain (optional but beneficial).