Senior Product Manager

Cityblock Health

About The Position

Healthcare in the United States is failing the people who need it most. The members we serve - Medicaid and Dual eligible individuals - have consistently been last in line for meaningful innovation, and the consequences show up in health outcomes, cost, and trust. Cityblock exists to change that. We're building the operating system for value-based care: integrated medical, behavioral, and social care for complex populations, delivered by real clinical teams and powered by a technology platform designed to move outcomes - not just report on them. We serve hundreds of thousands of members under full-risk and shared-savings contracts, which means the quality of our care and the rigor of our technology are measured in the same place: in whether members' lives get better. This is also one of the few environments where AI is being applied to healthcare in production rather than in pilots and demonstrations. Large language models, predictive risk models, and workflow automation are shipping into the tools our frontline care teams use every day. Risk stratification, next-best-action recommendations, care orchestration are genuinely unsolved problems, and we are inventing the patterns for how they should work at scale. About the Role We're hiring a Senior Product Manager to own end-to-end product delivery - from intake and discovery through scoping, build, release, and measurement - working in partnership with the Tech org, including Design, Engineering, Data Analytics, and Data Science. You'll own the "what" and the "why," translate business and clinical needs into prioritized, well-scoped work, and ensure the technology we build moves the needle on member outcomes and business KPIs. This role is also explicitly AI-forward. We expect our PMs to use AI as a default first step in their workflow as a core part of their craft.

Requirements

Bachelor's degree
7+ years of product management experience, with demonstrated ownership of shipping outcomes (not just features).
Track record of products that moved a measurable business or user metric.
Experience in a complex, multi-stakeholder environment such as healthcare, fintech, operations-heavy SaaS, platform products, or similar.
Clinical and care model literacy.
Healthcare experience required. However, the ability to build domain fluency quickly is essential. Within your first few months you should be conversant in how care programs, eligibility logic, and risk signals interact and be able to translate between clinical operations stakeholders and engineering.
Strong written craft. You can write a PRD that a clinician, an executive, or someone in the tech org can all read and act on.
Quantitative fluency. You're comfortable reading a dataset, writing or reviewing SQL, interrogating a dashboard, and defining success metrics before development begins. You can push back when a proposed metric won't actually prove the thing it claims to prove.
Data Platform and data governance fluency. You are comfortable working at the intersection of clinical operations and data infrastructure. You can navigate complex data dependencies, interrogate data models, and scope product work that involves data pipeline changes.
Experience working in a modern delivery environment two-week cycles, Linear or equivalent, continuous release, transparent backlogs. You know how to run a standup that isn't a status meeting.
Comfort with ambiguity and discovery. You know when a request needs a lightweight validation pass versus a deeper behavioral science-led research effort, and you can make the call.
Stakeholder management at senior levels. You can hold a firm line with executives when a request is operational rather than technological, and keep the relationship intact.
Partner and payer complexity. You understand how external data contracts, payer file delivery timelines, compliance requirements, and roster maintenance processes impact product decisions. You can coordinate effectively across Partner success, Actuary, and external payer partners without losing momentum on delivery.
AI-Forward Mindset: Specifically, you should already be doing several of the following in your current role or be ready to day one: Using AI (Claude, Cursor, or similar) to generate the first draft of PRDs, scope proposals, backlogs with draft acceptance criteria, stakeholder comms, and process diagrams.
Using AI to interrogate unfamiliar datasets or data file specs — explain schemas, flag ID mapping issues, and summarize what a file is actually telling you.
Using AI to pressure-test a roadmap narrative or surface gaps before planning.
Using AI to draft severity assessments and triage recommendations on bug reports and escalations before routing them.
Having an opinion on agentic workflows, automated status reporting, and how AI should be integrated into a PM's operating model. We run an internal AI agent that automates our Linear → Notion → Slack pipeline; you'll be expected to use it, improve it, and build on it.

Responsibilities

End-to-end product delivery including intake, discovery, scoping (PRDs), estimation, build, release, and post-launch measurement.
Business case evaluation and prioritization score new requests using a prioritization framework and partner with tech leadership to sequence work against company milestones, KPIs, and KTLO commitments.
PRDs and scoping artifacts that clearly define the problem, proposed approach, requirements, success metrics, dependencies, and risks. Living documents that stay current from discovery through deployment and include prototypes.
Stakeholder management and roadmap transparency Our backlog lives in Linear, your docs live in Notion, and your stakeholders always know where things stand.
Cross-functional coordination with Engineering, Data Science, Data Engineering, Data Analytics, Design across the full delivery lifecycle.
Say/Do accountability If we commit to it, we deliver it, we measure it, and we publish the result, including when we fall short.
Post-release measurement Partner with Data Analytics to evaluate success metrics against targets defined at intake, and feed learnings back into the next planning cycle.