Vp Jobs

About The Position

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.

Benefits

• health insurance
• dental insurance
• vision insurance
• flexible PTO
• work from home options
• professional development budget
• support continuing education