Data Scientist

Key Responsibilities

• Design and develop end-to-end classical ML pipelines — from feature engineering to model deployment and monitoring
• Build demand forecasting models leveraging external data signals (weather, events, seasonality) alongside historical sales data, at store/category/SKU level with 1–14 day horizons
• Develop ML-based risk scoring models across multiple fraud and exception scenarios, replacing manual rule-based processes with adaptive, dynamic thresholds
• Deliver daily prioritized outputs (investigation lists, inventory signals) that reduce detection and decision cycles from weeks to days
• Own model validation, threshold tuning, false positive reduction, and ongoing performance monitoring in production
• Collaborate with data engineers on feature pipelines using Microsoft Fabric Lakehouse, Dataflow Gen2, and OneLake
• Participate in iterative pilot-to-production delivery cycles with structured feedback incorporation
• Communicate model outputs and business impact clearly to both technical teams and business stakeholders

Required Skills & Experience

• 8–12 years of hands-on Data Science experience with a strong foundation in classical ML
• Proficiency in supervised and unsupervised ML techniques — gradient boosting, regression, classification, anomaly detection, time-series forecasting (XGBoost, LightGBM, scikit-learn, Prophet, statsmodels)
• Strong hands-on experience with Microsoft Fabric — ML Experiments, Notebooks (Python/PySpark), Lakehouse, Pipelines, and Dataflow Gen2
• Solid Python programming skills with experience building production-grade ML code
• Experience with MLflow for experiment tracking, model registry, and lifecycle management (native within Fabric)
• Proven experience building time-series forecasting models at granular levels (store, SKU, or category)
• Experience with anomaly detection and risk/fraud scoring models in retail or financial domains
• Strong skills in feature engineering, cross-validation, model interpretability (SHAP, LIME), and drift detection
• Nice to have: integrating external data enrichment sources (weather APIs, economic indicators, third-party signals)
• Exposure to Power BI or Fabric-native reporting for operationalizing model outputs to business users
• Experience with irregular or non-reorderable inventory environments