Senior Data Scientist

About the role

ABOUT PROBABLY GENETIC

Probably Genetic is changing the lives of patients living with severe, complex diseases. Our data platform is used by drug developers and patient advocacy groups to develop and launch treatments for these patients. Our technology discovers undiagnosed patients online, analyzes their disease state using machine learning and at-home testing, and enables compliant communication with patients. In doing so, we help patients access diagnoses, clinical trials, and treatments as early as possible.

We are a tight-knit group of hard-working, ambitious problem solvers united by a mission greater than ourselves. We do well by doing right by patients. We are developing some of the most cutting-edge solutions in healthcare, and our roadmap is packed with innovations in bioinformatics, AI, and drug development. We have built a lean, all-star team to help us bring our vision to life, and we want you to be a part of it.

Probably Genetic has raised multiple rounds of funding from Silicon Valley’s best investors, including Threshold, Khosla, and Y Combinator, and offer competitive salaries, comprehensive benefits, and meaningful early stage equity.

ABOUT THE ROLE

We are looking for a Senior Data Scientist who will own some of the most consequential diagnostic AI in rare disease: building, validating, and operationalizing the models that help us find and diagnose patients who have never had a name for their disease, powering the analytical rigor behind our testing programs, and shaping how we use data to make smarter product decisions.

WHAT YOU WILL 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