Product Area Lead

Formation Bio•Boston, MA

5d•Hybrid

About The Position

Formation Bio is hiring Product Leads to own major problem domains across our drug development platform - for example, how we evaluate and acquire drug assets, how we design and run clinical trials, or how we build regulatory submissions. You will report to the CTO. This is a product leadership role, but not a traditional one. You won't be writing specs for engineers to build. You'll own a business domain: set the strategy, define what to build, lead a cross-functional team through delivery, and be measured on whether the work actually changes outcomes for the drug programs and business functions you serve. Your team will include engineers, data scientists, and product managers (ranging from senior ICs to managers). You will be embedded with our clinical, translational, regulatory, and business development leaders - understanding their problems deeply enough to identify where technology can make a material difference. That means learning what makes a drug asset attractive, what drives a trial design decision, what the FDA cares about, and where the real time and cost sinks are. You should be able to drive value in a clinical review meeting in the same way you drive value in a sprint planning session. We are not building consumer software. We are building tools, models, and systems that make specific drug development decisions faster and better - prediction models that assess drug candidate viability, AI-driven workflows that compress diligence timelines, and platforms that let non-engineers build their own analytical tools. You will own a slice of that work and be accountable for its impact.

Requirements

10+ years of experience in tech delivery with progressively broader scope, including at least one role where you led product or technology strategy and were held accountable for business outcomes.
Life sciences experience: You've built products in biotech, pharma, or a related industry. You understand the drug development lifecycle well enough to have informed opinions about where technology does and doesn't help. You've worked in an environment shaped by regulatory constraints and scientific uncertainty.
Strategic product thinker: You define product direction based on business outcomes, not feature velocity. You've owned a product area where success was measured in terms the business cared about - time saved, decisions improved, cost reduced - not just user engagement or ship rate.
AI fluency: You can evaluate AI/ML approaches, make build-vs-buy decisions on AI capabilities, and push your team to use AI where it creates real leverage. You've deployed or led the deployment of LLM-based or agentic systems in a professional context.
Technical fluency: You can engage deeply with engineers and data scientists on approach, architecture, and trade-offs. You can evaluate whether an ML model is solving the right problem, whether a data pipeline is reliable enough, and whether an engineering approach will scale.
Cross-functional leadership: You've led teams that included engineers, data scientists, and product managers. You know how to hire, develop, and hold people to high standards.
Business fluency: You can discuss clinical trial design, asset valuation, regulatory strategy, and competitive dynamics with senior leaders without retreating to technical jargon. You've been in rooms where the conversation was about business decisions, not product decisions, and you contributed.
Comfortable with ambiguity and autonomy: You are highly adaptable and curious. You've worked in early-stage companies, on transformation efforts or new initiatives where the scope was unclear and the playbook didn't exist. That energizes you rather than stalls 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 product strategy for a defined problem domain (e.g., drug candidate evaluation, clinical trial execution, regulatory intelligence). Define what to build, what to deprioritize, and how to measure success.
Drive adoption of AI across your domain. We use LLMs, agentic workflows, and autonomous systems extensively. You should be pushing what's possible, not waiting for the technology teams to propose it.
Design systems that domain experts across the company can build on directly. Non-engineers are already creating their own technology - your solutions should accelerate that, not bottleneck it.
Lead a cross-functional team spanning engineering, data science, and product. Set the roadmap and make resourcing trade-offs based on business impact.
Work directly with our core functional teams as a strategic tech partner. Translate their problems into technology solutions - not by taking feature requests, but by understanding the domain well enough to see opportunities they may not.
Guide the data science and engineering approach within your domain, including real-world evidence analysis, prediction pipelines, clinical strategies, and dataset evaluation. You need enough technical judgment to know when an approach is sound and when to push back.
Maintain awareness of regulatory constraints. You don't need to be a regulatory expert, but you need to build products that work within GxP requirements and clinical data governance standards.