Data Scientist

SIDRAM TECHNOLOGIES; Seattle (WA); Hybrid; USD 140,000 - 190,000; Full time

Job summary

A technology staffing firm seeks a Senior Data Scientist for a hybrid role in Seattle, Washington. The candidate will design and operationalize predictive models in Microsoft Fabric, focusing on demand forecasting and risk scoring in retail. Ideal applicants have 8-12 years of Data Science experience, strong skills in classical ML, and proficiency with Microsoft Fabric's tools. This position includes working on real-world use cases and requires excellent communication with technical teams and stakeholders.

Qualifications

• 8–12 years of hands‑on Data Science experience with a strong foundation in classical ML.
• Strong hands‑on experience with Microsoft Fabric, building production‑grade ML code.
• Proficiency in supervised and unsupervised ML techniques.
• Solid Python programming skills with experience building production‑grade ML code.
• Experience with MLflow for experiment tracking, model registry, and lifecycle management (Fabric).

Responsibilities

• Design and develop end‑to‑end classical ML pipelines.
• Build demand forecasting models using external data signals.
• Deliver daily prioritized outputs that reduce detection cycles.
• Deliver daily prioritized outputs (investigation lists, inventory signals) to reduce detection and decision cycles.
• Own model validation, threshold tuning, false positive reduction, and ongoing performance monitoring in production.
• Collaborate with data engineers on feature pipelines using Fabric Lakehouse, Dataflow Gen2, and OneLake.
• Participate in iterative pilot‑to‑production delivery cycles with structured feedback.
• Communicate model outputs and business impact clearly to both technical teams and business stakeholders.

Skills

Machine Learning

Azure Machine Learning

Artificial Intelligence

MLOps

AI Solution Architecture

Microsoft Azure

Microsoft Fabric

Tools

Python

PySpark

MLflow

Lakehouse

Pipelines

Dataflow Gen2

Notebooks

Job description

Hi Rachael from SidRam Tech, we have an urgent position for a Senior Data Scientist at our Direct client Savers Inc. This role is based in Seattle, Washington and offers a hybrid work model, 2 days a week in office.

Title: Sr Data Scientist

Client: Savers Inc

Location: Seattle, Washington (Hybrid, 2 days a week)

Skills: Machine Learning, Azure Machine Learning, Artificial Intelligence, MLOps, AI Solution Architecture, Microsoft Azure, Microsoft Fabric

Interview process: Two with Tavant + Client round

Note: at least one in-person meeting is mandatory.

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

Contact: Rachael
IT Services | Development | Staffing
Email: [e-mail]
Direct: 4705239688

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