VP, Data

At a high level, this person will own the entire data function at 9amHealth — data engineering and platform, analytics and BI, and data science / ML / AI. 9amHealth is a virtual care company serving members managing chronic conditions like diabetes, hypertension, cholesterol, and weight management, and data is one of the most direct levers we have on member outcomes, clinical decision-making, and how efficiently we operate. What makes the role unique is that the data function doesn’t sit in isolation. It powers the member app, the internally built EMR, the operational tooling care teams use every day, and a growing set of AI-assisted workflows. Decisions made by the VP of Data — what we instrument, how we model the business, what we automate with ML — directly shape what members experience and what clinicians do.

Responsibilities

Own the entire data function at 9amHealth — data engineering and platform, analytics and BI, and data science / ML / AI; Set and communicate data vision, strategy, and roadmap; Review core company metrics — engagement, retention, clinical outcomes, unit economics — and shape what gets prioritized; Partner with Product and Clinical on experiment design, sample sizing, and reading results responsibly; Coach and develop managers and ICs across the data org; Make tradeoff decisions between platform investment, analytics throughput, and ML/AI bets; Work with engineering leadership on data architecture, real-time vs. batch needs, and model deployment; Review ML model performance, drift, and clinical safety considerations before shipping into care workflows; Represent data in board conversations, investor updates, and cross-functional planning

Requirements

Owned a full data function end-to-end at scale; Speak clearly about strategy, outcomes, tradeoffs, and team building across data engineering, analytics, and ML; Have led data orgs through meaningful scale (early growth through multi-team); Have built and matured data platforms — ingestion, warehousing, modeling, governance — without over-engineering; Have shipped ML or applied AI into a real product, not just into a notebook; Have operated in ambiguity and built clarity from it (definitions, ownership, metric trees, source of truth); Move quickly and independently and push teams to do the same; Are comfortable making decisions with imperfect data; Have strong product and business instincts in addition to technical depth; Understand experimentation, causal inference, and the limits of A/B testing in healthcare contexts; Have worked with PHI / HIPAA and understand the compliance, privacy, and security implications of data work in healthcare; Can defend decisions clearly to the executive team, the board, and the broader org; Have hired, coached, and leveled up data engineers, analytics engineers, analysts, data scientists, and ML engineers; Are genuinely fluent with modern AI-assisted tooling, actively using tools like Cursor, Claude, v0, or Lovable in their own workflow; Have hands-on opinions about where LLMs and agents accelerate data and analytics work, where they’re still risky in a clinical setting, and can set the standard for how the broader data and engineering org adopts AI responsibly; Technically conversant and able to make credible architecture and platform decisions

Nice to have

Healthcare or regulated-industry experience is a strong plus but not required

Benefits

Health, dental, and vision insurance; Flexible PTO; Work from home options; Professional development budget; Support for continuing education; Opportunity to make a meaningful impact on chronic disease management and patient outcomes; Collaborative, mission-driven culture focused on improving healthcare accessibility and affordability