Data Scientist

What You’ll Do

• Build Predictive Models: Develop and refine machine learning models to solve critical business problems, such as predicting patient churn, estimating operational volume, or scoring advocate quality.
• Drive Forecasting Rigor: Own the development of time-series forecasts that guide our capacity planning and resource allocation.
• Design & Analyze Experiments: Lead the design and analysis of A/B tests and causal inference studies.
• Uncover Deep Insights: Apply advanced statistical methods to complex datasets to find patterns.
• Productionize Solutions: Work closely with Data Engineers to take your models from a local notebook to a production environment.
• Communicate Complexity: Translate complex statistical findings into clear, actionable narratives for non-technical stakeholders.

What You Bring

• Statistical Expertise: Strong background in statistics, probability, and mathematics (e.g., hypothesis testing, regression analysis, time-series forecasting).
• Python & ML Proficiency: Fluent in Python and its data science ecosystem (pandas, scikit-learn, statsmodels, NumPy). Write clean, reproducible code.
• Forecasting Experience: Hands-on experience with time-series analysis and forecasting techniques (e.g., ARIMA, Prophet, exponential smoothing).
• Advanced SQL: Write complex queries to wrangle data from Snowflake.
• Preferred Masters Degree or PhD: Data Science, Applied Science, or related fields.
• Data Privacy Awareness: Familiar with best practices for handling PHI and PII.
• Startup DNA: Self-starter comfortable with ambiguity.

Bonus Points

• Deployment Experience: Experience using tools like Docker, Airflow, or MLflow.
• Healthcare Experience: Working with healthcare or insurance claims data.
• NLP & Unstructured Data: Applying NLP techniques.
• Marketplace Matching: Designing matching algorithms or ranking systems.
• dbt & Engineering Skills: Comfort reading or writing dbt models.