Senior Data Scientist
Location: Seattle, Washington
Preferred Hybrid, 2 days a week- however we are open to remote
Skills: Machine Learning, Azure Machine Learning, Artificial Intelligence, MLOps, AI Solution Architecture ,Microsoft Azure ,Microsoft Fabric
Job Description
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
Nice to Have:
- Experience integrating external data enrichment sources (weather APIs, economic indicators, third-party signals)
- Familiarity with retail loss prevention, exception-based reporting, or shrinkage analytics
- Exposure to Power BI or Fabric-native reporting for operationalizing model outputs to business users
- Knowledge of Azure ML and its relationship with Microsoft Fabric ML capabilities
- Experience with irregular or non-reorderable inventory environments
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