Director/Senior Director, Product - Applied Data Science

Formation Bio•Boston, NY

1d•Hybrid

About The Position

This role sits on Formation's Asset Acquisition & Diligence (AAD) team. In the course of evaluating and completing diligence on individual drug assets, our Data Science team runs sophisticated one-off analyses (e.g. a novel algorithm run against a specific gene or indication, a causal model built for a specific therapeutic hypothesis). Much of that work directly shapes a specific deal decision in the moment. This role exists to take these and scale them into strategies and tools that can provide an organization level advantage. You will own the judgment call of which asset-evaluation and diligence analyses have generalization potential across the AAD team's sourcing and evaluation work, and what form that generalization should take: sometimes a landscape analysis (the same method run not on one gene, but on every gene, surfaced as a standing analysis that informs sourcing rather than a product), sometimes a fully engineered, scaled tool built in partnership with Data Science and Engineering. You are the connective tissue between one-off diligence work and durable diligence and sourcing capability. This is a hybrid science and product role on AAD. You will set technical and product direction for a working team of data scientists and engineers who continue to report to their functional leads. You'll need enough technical depth to judge when a diligence method holds up at scale, and enough product judgment to know when “scale” means a single exhaustive analysis rather than a maintained tool - all in service of how Formation sources and evaluates drug assets.

Requirements

7+ years of experience in data science, applied science, or a technical product role with progressively broader scope, including at least one role where you were accountable for turning analytical work into a deployed capability.
Life sciences experience: you've worked in biotech, pharma, or a related industry, and understand the drug development lifecycle well enough to have informed opinions about where a computational method holds up and where it doesn't.
Strategic thinker: you decide what's worth scaling based on business and scientific impact, not novelty or ease of building. You're as comfortable saying a finding shouldn't scale as you are greenlighting one that should.
AI/ML fluency: you can evaluate whether a method remains valid at exhaustive scale, make build-vs-buy calls on tooling, and push for AI/ML approaches where they create real leverage.
Technical fluency: y ou can engage deeply with data scientists and engineers on method, architecture, and trade-offs. You know when a statistical approach breaks down outside the single case it was built for.
Cross-functional influence: you've directed or coordinated technical teams, including engineers and data scientists, and you earn trust through technical credibility.
Business fluency: you can discuss diligence findings, asset valuation, and scientific risk with senior leaders.
Comfortable with ambiguity and autonomy: this is a new function at Formation, and the scope of what gets scaled and how will be defined in part by you.
Mission-driven: you care about getting medicines to patients faster. That's what this company exists to do, and it matters to you.

Responsibilities

Own the strategy for scaling Formation's data science work for asset acquisition and diligence.
Decide which one-off analyses have generalization potential, in what form (landscape analysis vs. scaled tool), and how to measure success.
Direct a working team spanning Data Science and Engineering day to day.
Set the roadmap and sequence work based on business impact, coordinating with functional leads on resourcing.
Execute against that roadmap with clear lines to business impact.
Work directly with Data Science and Business Development as strategic partners.
Translate scientific findings into a scaled roadmap, not by taking feature requests, but by understanding the science well enough to see generalization and alpha opportunities.
Guide the technical approach on methods being scaled, including statistical validity at exhaustive scale, pipeline design, and dataset evaluation.
Work with scientific and technical teams to identify any new opportunities for AI to impact Formation’s asset acquisition and diligence process and be able to pitch those to stakeholders with a clear explanation of resourcing and business impact.