Senior Data Scientist

• 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
• We are looking for a few specific things that will help you succeed in this role:
• 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
• Some things that are not required, but you will learn on the job:
• Experience with Databricks or similar lakehouse/ML platform environments
• Familiarity with synthetic data generation techniques
• Domain knowledge in healthcare, rare disease, genomics, or clinical research
• Experience with MLOps tooling and building observability infrastructure from scratch
• Exposure to biopharma or insurance analytics use cases
• As with all new hires at Probably Genetic, you will also need to be:
• A good person. We work with some of the most marginalized populations on the planet and empathy is key

None flagged.