VP of Data

9amHealth

11d•Remote

About The Position

At 9amHealth, the VP of Data will own the entire data function, encompassing data engineering and platform, analytics and BI, and data science/ML/AI. This role is crucial as data directly influences member outcomes, clinical decision-making, and operational efficiency in managing chronic conditions like diabetes, hypertension, cholesterol, and weight management. The data function is deeply integrated, powering the member app, EMR, operational tooling, and AI-assisted workflows. Decisions made by the VP of Data will significantly shape member experiences and clinician actions. This is a strategic leadership hire for a company scaling its member base, expanding its clinical model, and investing heavily in AI-assisted care. The ideal candidate will set the vision for the data organization, build and grow the team, and partner with the executive team to create a data, analytics, and ML roadmap. The role requires autonomy, a high standard of data craft and rigor, and the ability to evolve the organization through scaling, especially with the increasing importance of AI-assisted workflows and intelligent care experiences. This is not a hands-on execution or narrow analytics role; it demands strategic thinking about the member journey, clinical operations, business economics, and the operational implications of data and ML decisions, leading a multi-disciplinary team.

Requirements

Owned a full data function end-to-end at scale.
Experience leading data organizations through meaningful scale (early growth through multi-team).
Experience building and maturing data platforms (ingestion, warehousing, modeling, governance) without over-engineering.
Experience shipping ML or applied AI into a real product.
Experience operating in ambiguity and building clarity (definitions, ownership, metric trees, source of truth).
Ability to move quickly and independently and push teams to do the same.
Comfort making decisions with imperfect data.
Strong product and business instincts in addition to technical depth.
Understanding of experimentation, causal inference, and the limits of A/B testing in healthcare contexts.
Experience working with PHI/HIPAA and understanding the compliance, privacy, and security implications of data work in healthcare.
Ability to defend decisions clearly to the executive team, the board, and the broader organization.
Experience hiring, coaching, and leveling up data engineers, analytics engineers, analysts, data scientists, and ML engineers.
Genuinely fluent with modern AI-assisted tooling (e.g., Cursor, Claude, v0, Lovable).
Hands-on opinions about where LLMs and agents accelerate data and analytics work, and where they are risky in a clinical setting.
Ability to set the standard for how the broader data and engineering org adopts AI responsibly.
Technical conversancy to make credible architecture and platform decisions.
Adaptability, systems thinking, and judgment.
Experience leading a distributed team and comfortable building rituals and writing artifacts that keep a remote, multi-time-zone org aligned.

Nice To Haves

Healthcare or regulated-industry experience.

Responsibilities

Own the entire data function: data engineering and platform, analytics and BI, and data science/ML/AI.
Set vision, strategy, and roadmap for the data organization.
Build and grow the data team.
Partner with the executive team to translate company strategy into a data, analytics, and ML roadmap.
Raise the bar on data craft and rigor.
Evolve the organization as the company scales, particularly with AI-assisted workflows.
Think strategically about the member journey, clinical operations, business economics, and operational implications of data and ML decisions.
Lead a multi-discipline team including data engineers, analysts, data scientists, and ML engineers.
Collaborate with the CEO and executive team on company strategy, metrics, and reporting.
Review core company metrics (engagement, retention, clinical outcomes, unit economics) and shape prioritization.
Partner with Product and Clinical on experiment design, sample sizing, and responsible results interpretation.
Coach and develop managers and individual contributors across the data organization.
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 deployment into care workflows.
Represent data in board conversations, investor updates, and cross-functional planning.
Drive clarity and decisions even with incomplete information, messy data, and imperfect model evaluations.
Own and grow the data organization end-to-end: data engineering and platform, analytics engineering and BI, data science, and applied ML/AI.
Ensure data is embedded in product, clinical, and operations decision-making.
Lead a distributed team effectively and build rituals for alignment across multiple time zones.
Speak clearly about strategy, outcomes, tradeoffs, and team building across data engineering, analytics, and ML.
Build and mature data platforms (ingestion, warehousing, modeling, governance) without over-engineering.
Ship ML or applied AI into a real product.
Operate in ambiguity and build clarity (definitions, ownership, metric trees, source of truth).
Move quickly and independently, and push teams to do the same.
Make decisions with imperfect data.
Understand experimentation, causal inference, and the limits of A/B testing in healthcare.
Work with PHI/HIPAA and understand compliance, privacy, and security implications.
Defend decisions clearly to the executive team, board, and broader organization.
Hire, coach, and level up data engineers, analytics engineers, analysts, data scientists, and ML engineers.
Set the standard for responsible AI adoption within the data and engineering organization.
Balance speed, ambiguity, and complexity at the leadership level.
Balance member clarity, engagement, and clinical outcomes with clinical safety, model evaluation, and operational efficiency.
Balance business economics and unit economics with technical constraints, data platform investment, and tech-debt tradeoffs.
Balance AI-assisted automation versus human care workflows, including when not to automate.
Manage PHI, HIPAA, and privacy/security expectations.
Create structure for the organization when processes are not yet established.
Shape how chronic care is delivered and measured.
Build data, analytics, and ML holistically across member experience and clinical/operational infrastructure.