Data Scientist - Data & Analytics

Key Responsibilities

• Modeling & Evaluation: Build and evaluate models using GLMs, GBMs, and related approaches. Assess model performance and stability, diagnose overfitting, and document findings clearly for technical and non-technical audiences.
• Third-Party Data & Vendor Support: Assist in managing third-party data relationships, including data intake, validation, and iterative testing. Engage with external vendors to resolve discrepancies and ensure data quality.
• Business Partnership & Communication: Collaborate with business stakeholders to understand analytical objectives and contribute to translating results into clear recommendations. Develop comfort presenting findings and explaining tradeoffs to partners with varying levels of technical fluency.
• Analytical Execution: Contribute to process improvement and automation efforts to reduce manual effort and increase analytical throughput. Support work across multiple lines of coverage with attention to rigor and consistency.
• Monitoring & Governance: Help define and track metrics for classification, forecasting, and business KPIs. Support A/B testing, monitor for drift, and contribute to compliance, privacy, and responsible modeling standards.
• Continuous Learning:
  
  Stay current on developments in ML, statistical modeling, and best practices. Build familiarity with the broader analytical toolkit and contribute to reusable templates and documentation.

Required Skills & Experience

Modeling & Statistics

• Working knowledge of GLMs and GBMs in Python, with an understanding of when each approach is appropriate (e.g., GBMs for exploration and interaction detection; GLMs for interpretability and implementation readiness).
• Ability to assess model stability, identify overfitting, and communicate results and tradeoffs clearly.
• Familiarity with ML lifecycle best practices including documentation, version control (GitHub), and experiment tracking (e.g., MLflow).

Data & Vendor Support

• Experience with data validation, quality checks, and working across multiple data sources simultaneously.
• Comfortable engaging with external vendors or data providers to ask clarifying questions and resolve data issues.

Business Communication

• Ability to present analytical findings clearly to both technical and non-technical audiences.
• Developing skill in translating model results into actionable recommendations, including communicating uncertainty or limitations honestly.

Technical Foundations

• Bachelors or Masters degree in Computer Science, Mathematics, Data Science, or a closely related discipline.
• Experience in statistical modeling and machine learning using Python (pandas, NumPy, scikit-learn) with strong SQL skills.
• Across the modeling lifecycle: problem framing, experiment design, evaluation, and validation.
• Experience using Git and Unix-based development environments with reproducible analytical workflows.
• Familiarity with model monitoring concepts including drift detection and performance tracking.
• Some exposure to cloud-based platforms (Vertex AI, SageMaker, or Azure ML) is a plus.
• Familiarity with enterprise governance expectations including compliance, privacy, and model documentation standards.