Data Scientist I

Generate Biomedicines•Somerville, MA

11d

About The Position

We are looking for a pragmatic, curious data scientist who wants to learn how data can improve clinical trials and help turn careful analysis into better decisions. This person learns quickly, works well in environments where data security and governance are essential, and can help move from hypothesis to evidence with guidance from experienced clinical, statistical, and engineering partners. They will contribute to focused explorations across statistical modeling, trial operations, data quality, and decision support, helping test whether promising ideas are useful, trustworthy, and actionable. As the first Generator in this role, they will work closely with existing members of the Clinical AI Team, including a data engineer, a Bayesian-trained statistician, and a customer-focused product manager. They will be expected to bring initiative, care, and follow-through, while learning the habits of rigorous, transparent, decision-oriented clinical data science. They will also help establish good working practices for this new capability through careful, reproducible, well-communicated analysis. The data scientist will use their skills to help improve how clinical trials are planned, monitored, and understood. You will be part of a small team working to make clinical development more evidence driven. Your analyses, prototypes, and evidence packages will help the people running our trials make better decisions, with support from clinical, statistical, product, and engineering partners. This is not a reporting role, and the deliverable is not dashboards. The real deliverable is trustworthy evidence: careful analysis, clear communication, and follow-through that turn raw data into insights people can act on. This role is a good fit for someone who wants to work on meaningful clinical problems, develop ownership over time, and see careful analysis influence decisions in the real world. Here's how you will contribute: Trustworthy data. You will help the team work from data that is accurate, current, well-understood, and honest about its limitations. Because your work may inform important clinical and operational decisions, quality, provenance, and transparency matter. Turning data into evidence. You will contribute a quantitative, model-minded approach to well-scoped clinical and operational questions, working closely with senior statistical and clinical partners. You will learn to reason carefully under uncertainty, evaluate assumptions using evidence, and help the team distinguish between signals, hypotheses, and verified facts. Learning from decisions. You will not stop at producing an analysis. You will help track how analyses are used, learn what happened after decisions were made, and use that feedback to make future work stronger. Building with others. You will work closely with clinical, statistical, engineering, and product partners to understand the real-world context behind the data. Good answers will often come from combining technical rigor with curiosity, humility, and collaboration.

Requirements

A foundation in applied statistics, with curiosity about Bayesian inference, hierarchical and small-data modeling, calibration, experimental design, A/B testing, and causal inference.
Experience with Python, pandas, and SQL, with the ability to work with multi-source datasets and contribute to reproducible, well-documented analysis workflows.
Experience working with messy, multi-source data and turning it into clear, well-supported analysis that others can trust.
Ability to communicate what is known, what is assumed, what is missing, and what confidence we should have.
Care about the difference between a signal, a hypothesis, and a verified fact, and transparency about provenance and confidence.
Comfort working across functions, asking good questions, and translating technical analysis into practical implications.
Advanced degree in a quantitative / scientific field (e.g. MS or PhD)

Responsibilities

Help the team work from data that is accurate, current, well-understood, and honest about its limitations.
Contribute a quantitative, model-minded approach to well-scoped clinical and operational questions, working closely with senior statistical and clinical partners.
Learn to reason carefully under uncertainty, evaluate assumptions using evidence, and help the team distinguish between signals, hypotheses, and verified facts.
Help track how analyses are used, learn what happened after decisions were made, and use that feedback to make future work stronger.
Work closely with clinical, statistical, engineering, and product partners to understand the real-world context behind the data.
Help establish good working practices for this new capability through careful, reproducible, well-communicated analysis.