Summary
What you’ll impact
The Senior Data Scientist will own the end-to-end development, validation, and operationalization of predictive diagnostic AI models for rare disease detection, and will work closely with data engineering to build scalable data foundations. This role involves running prospective testing experiments, building synthetic patient data pipelines, optimizing patient intake with NLP, and establishing MLOps infrastructure while conducting strategic analyses and blue‑sky research.
Responsibilities
What you'll do
- Own the end-to-end development, validation, and operationalization of PG's predictive diagnostic AI models — from feature engineering through production deployment – that power program eligibility decisions and clinical decisions for patients
- Run prospective testing experiments: apply diagnostic models to undiagnosed patients, coordinate testing, and track outcomes to continuously improve model performance
- Build and maintain PG's synthetic patient data pipeline, a critical deliverable for our research programs, and key input to our own model development lifecycle
- Optimize our patient intake experience using NLP and multimodal data analysis to determine which questions to ask, in what order, to maximize data quality and conversion
- Own API usage and cost optimization across PG's AI stack, including prompt engineering, model evaluation, and ongoing performance monitoring
- Conduct ad hoc strategic analyses that inform product prioritization, causality assessment, and generate customer-facing program insights
- Establish MLOps infrastructure: model monitoring, drift detection, API observability, and lightweight but durable operational processes
- Have the freedom to conduct blue sky research initiatives aimed at creating value from our data
- Work with Data Engineering to build a robust, scalable data foundation that supports all of the above
Requirements
What you’ll bring
- 7+ years of experience in data science, machine learning engineering, or a closely related field
- Strong Python proficiency and fluency across the core data science stack: pandas, NumPy, scikit-learn, PySpark, and SQL
- Demonstrated end-to-end ML experience: you have taken models from problem definition through feature engineering, validation, deployment, and monitoring in a production environment
- Experience with NLP techniques and applying language models to real-world problems
- Comfort with prompt engineering and evaluating external AI API performance (e.g., OpenAI)
- A track record of operating with high ownership in lean, fast-moving environments where you have had to build structure as much as execute within it
- Strong analytical communication skills — you can translate complex model outputs and data findings into clear, actionable narratives for technical and non-technical audiences alike
- A good person. We work with some of the most marginalized populations on the planet and empathy is key
- Patient-focused and motivated to have a lasting, positive impact on humanity
- Comfortable in a fast-paced, often ambiguous environment with rapid change
- Action-oriented and excited to build a company from the ground up