VP of Data (Vienna)

About 9amHealth

9amHealth is an AI-enabled virtual specialty care platform focused on managing high-cost chronic conditions at scale. The company partners with employers, health plans, and pharmacy benefit managers to deliver comprehensive, cost-effective medical care for individuals living with obesity, diabetes, hypertension, and dyslipidemia. Members receive access to specialized clinicians, including endocrinologists, obesity medicine specialists, and clinical pharmacists, at-home lab testing, prescription medications, and lifestyle support.

9amHealth was founded in 2021 and is backed by leading healthcare investors like Define Ventures, SemperVirens, 7Wire Ventures, and The Cigna Group Ventures.

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.

Why the Role is Open

This is a strategic leadership hire. The company is scaling its member base, expanding its clinical model, and investing heavily in AI-assisted care workflows. We need a senior data leader who can set vision for the data org, build and grow the team, and partner with the executive team to translate company strategy into a coherent data, analytics, and ML roadmap.

We’re looking for someone who can operate with a lot of autonomy, raise the bar on data craft and rigor, and evolve the organization as we scale — especially as AI-assisted workflows and more intelligent care experiences become a bigger part of the platform strategy.

This isn’t a heads‑down execution role or a narrow analytics role. We want someone who can think strategically about the member journey, clinical operations, business economics, and the operational implications of data and ML decisions — and who can lead a multi‑discipline team (data engineers, analysts, data scientists, ML engineers) to do the same.

What the Day-to-Day Looks Like

Day to day, the role is highly collaborative and fast-moving.

You’d work closely with:

• The CEO and executive team on company strategy, metrics, and reporting

• Data engineers, analytics engineers, analysts, data scientists, and ML engineers across the data org

• Product and Engineering leadership on instrumentation, experimentation, and ML in production

• Clinical leadership, care coordinators, and coaches on outcomes, quality measurement, and model evaluation

• Growth, marketing, finance, and operations leaders on the metrics that run the business

• Compliance and security partners on PHI handling, HIPAA, audit, and access controls

A typical week could involve:

• Setting and communicating data vision, strategy, and roadmap across data engineering, analytics, and DS/ML

• Reviewing the core company metrics — engagement, retention, clinical outcomes, unit economics — and shaping what gets prioritized

• Partnering with Product and Clinical on experiment design, sample sizing, and reading results responsibly

• Coaching and developing managers and ICs across the data org

• Making tradeoff decisions between platform investment, analytics throughput, and ML/AI bets

• Working with engineering leadership on data architecture, real‑time vs. batch needs, and model deployment

• Reviewing ML model performance, drift, and clinical safety considerations before anything ships into care workflows

• Representing data in board conversations, investor updates, and cross‑functional planning

We move quickly, so there’s an expectation that the VP can drive clarity and decisions even when the brief is incomplete, the data is messy, and the model evaluation isn’t clean.

Team / Collaboration Structure

The role reports directly to the CEO and is a member of the executive team. The VP of Data will own and grow the data organization end‑to‑end: data engineering and platform, analytics engineering and BI, data science, and applied ML / AI.

One thing worth highlighting is how cross‑functional the environment is. Data isn’t a service team that fulfills tickets — it’s embedded in how product, clinical, and operations decisions get made. Adding a new metric, surfacing a new lab value, or shipping an ML‑driven recommendation can meaningfully change what care teams do day to day, so we’re looking for a leader who naturally thinks in systems rather than just dashboards or models in isolation.

The engineering and product organization is distributed between San Diego and Vienna, plus remote teammates across the US. The VP will need to be effective leading a distributed team and comfortable building rituals and writing artifacts that keep a remote, multi‑time‑zone org aligned.

What We’re Looking For

The strongest candidates are people who have owned a full data function end‑to‑end at scale and can speak clearly about strategy, outcomes, tradeoffs, and team building across data engineering, analytics, and ML.

We’re especially interested in data leaders who:

• 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

We also want someone who’s genuinely fluent with modern AI‑assisted tooling. That doesn’t just mean having tried ChatGPT once or twice. We want a leader who actively uses tools like Cursor, Claude, v0, or Lovable in their own workflow, who has hands‑on opinions about where LLMs and agents accelerate data and analytics work, where they’re still risky in a clinical setting, and who can set the standard for how the broader data and engineering org adopts AI responsibly.

Healthcare or regulated‑industry experience is a strong plus but not required if the data and ML leadership chops are strong.

Coding Stack

The VP of Data doesn’t need to be hands‑on in the production codebase, but should be technically conversant and able to make credible architecture and platform decisions. Our current stack:

• Backend: AWS, MySQL, Java/Spring Boot, some Python, JSON/REST APIs

• Frontend: TypeScript, React, Capacitor/Ionic

Tools / Systems Worth Mentioning

We work in a modern, collaborative product environment. The tools we run on day to day:

• Apple Mac

• Google Workspace

• Zoom

• Slack

• Confluence

• Jira

• Miro

• Figma

• Mixpanel

• 1Password

• Zendesk

• HubSpot

• Rippling

AI-assisted tooling that comes up frequently in product, engineering, and data workflows: Cursor, Claude, v0, Lovable, and rapid prototyping environments. We’re much more interested in adaptability, systems thinking, and judgment than whether someone has used one exact tool for years.

Biggest Challenges in the Role

The biggest challenge is balancing speed, ambiguity, and complexity at the leadership level.

You’re leading data in healthcare, so the stakes are real. You’re balancing:

• Member clarity, engagement, and clinical outcomes

• Clinical safety, model evaluation, and operational efficiency

• Business economics and unit economics

• Technical constraints, data platform investment, and tech‑debt tradeoffs

• AI‑assisted automation versus human care workflows, including when not to automate

• PHI, HIPAA, and the privacy and security expectations of working with sensitive health data

• A distributed team across San Diego, Vienna, and remote US

And because the company is still scaling, there won’t always be a perfect brief, a clean dataset, a clear owner of every metric, or an established process before decisions need to get made. The VP of Data is often the person who has to create that structure for the rest of the org]]> <