Data Scientist

Summary

Ophelia is a healthcare startup providing evidence-based treatment for opioid use disorder through a telehealth platform. The Data Scientist will build and productionize statistical and machine-learning models for forecasting, experimentation, risk prediction, and clinical and business decision-making while partnering across teams to advance data-driven care.

Responsibilities

• Predictive & statistical modeling: Build, validate, and ship models - demand and clinical-capacity forecasting, no-show and retention risk, outcome and risk prediction - that directly inform clinical and business strategy
• Experimentation & causal inference: Design and analyze experiments and quasi-experiments so the organization can measure true impact, not just correlation
• Productionalizing models: Collaborate with Analytics Engineering and Technology teams to deploy models into robust pipelines (BigQuery, dbt, Dagster) with continuous monitoring, retraining, and drift detection - ensuring models operate as living products
• From ambiguity to rigor: Translate open clinical and operational questions into well-scoped analyses that are mathematically sound and clearly caveated
• Cross-functional partnership: Serve as the modeling partner for Clinical, Commercial, and Business Operations - surfacing high-leverage opportunities and communicating results in plain language
• Raising the bar: Act as the subject-matter expert for statistical and ML methods, championing rigorous, evidence-based decision-making across the team

Skills

• 3-5+ years of applied data science with a strong foundation in statistics, probability, experimentation, and machine learning
• Advanced proficiency in the Python ML/analysis stack (pandas, scikit-learn, statsmodels, gradient-boosting frameworks such as LightGBM/XGBoost, and forecasting methods)
• You turn complex modeling into clear narratives and "so-what" recommendations that a non-technical audience can act on
• You thrive in environments where data is semi-structured and projects span a wide variety of business domains, and you navigate ambiguity comfortably
• Comfortable and enthusiastic about using modern AI tools (e.g., Claude, Cursor, Copilot) to accelerate coding, analysis, and research, while applying the judgment to know when human review and validation matter most
• Familiarity with R
• Familiarity with resource-allocation, scheduling, or optimization problems (e.g., linear/integer programming, queuing theory, simulation)
• Interest in taking models from exploration into production pipelines with monitoring and retraining
• Orchestration experience (Dagster, Airflow)
• A passion for making evidence-based addiction treatment accessible
• Experience in a regulated industry (healthcare, fintech, etc.)

Qualifications

Must Haves

• 3-5+ years of applied data science with a strong foundation in statistics, probability, experimentation, and machine learning
• Advanced proficiency in the Python ML/analysis stack (pandas, scikit-learn, statsmodels, gradient-boosting frameworks such as LightGBM/XGBoost, and forecasting methods)
• You turn complex modeling into clear narratives and "so-what" recommendations that a non-technical audience can act on
• You thrive in environments where data is semi-structured and projects span a wide variety of business domains, and you navigate ambiguity comfortably
• Comfortable and enthusiastic about using modern AI tools (e.g., Claude, Cursor, Copilot) to accelerate coding, analysis, and research, while applying the judgment to know when human review and validation matter most

Nice to Haves

• Familiarity with R
• Familiarity with resource-allocation, scheduling, or optimization problems (e.g., linear/integer programming, queuing theory, simulation)
• Interest in taking models from exploration into production pipelines with monitoring and retraining
• orchestration experience (Dagster, Airflow)
• A passion for making evidence-based addiction treatment accessible
• Experience in a regulated industry (healthcare, fintech, etc.)

Benefits

• Fully remote position
• Opportunities for increased compensation annually, as long as the company performs well and meets its targets