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This is Rachael from SidRam Tech, We have an urgent position Sr Data Scientist @Seattle, Washington, with our Direct client. Kindly have a look at JD below and let me know your interest.
We are looking for a Senior Data Scientist with strong classical ML expertise to design, build, and operationalize predictive models within the Microsoft Fabric ecosystem. You will work on high-impact use cases spanning demand forecasting, risk scoring, and anomaly detection for large-scale retail environments — translating raw data signals into actionable business intelligence.
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
