Head of Data at AIOS — Remote, $150k-$300k/yr inc equity

AIOS (YC W20/S21)

3d•$150,000 - $300,000•Remote

About The Position

AIOS is building the world’s first full-stack AI doctor and is experiencing rapid growth, reaching $350M in annualized revenue and serving over 150k active patients. The company's strategy focuses on leveraging GLP-1s and targeting the large European healthcare market. AIOS has ambitious plans for scaling revenue to $1B by the end of 2026, $10B by 2028, and $100B by 2031, with a long-term vision of becoming a trillion-dollar healthcare company by enabling global, autonomous medical treatment. As a young, founder-led company, AIOS emphasizes a culture of continuous work and development. The Head of Data will play a crucial role in establishing and leading a world-class data team, owning the data stack end-to-end, and transforming the company's data foundation into a trusted operating system for decision-making.

Requirements

5+ years as a hands-on data individual contributor.
3+ years leading teams, including directly managing at least a couple of people.
Deep fluency in SQL and dbt.
Experience designing and operating a modern data platform (ingestion, cloud warehouse, transformation, reporting, monitoring).
Understanding of production data systems beyond the modeling layer (orchestration, incremental processing, dependencies, backfills, failure recovery, freshness, data-quality controls).
Knowledge of designing models that remain clean and understandable as the business changes, with attention to contracts, tests, lineage, ownership, documentation, and intuitive interfaces.
Ability to turn ambiguous business goals into precise metrics, with a deep understanding of funnels, cohorts, activation, retention, churn, revenue, and growth.
Heavy use of AI in personal work and strong instincts for making AI-assisted analysis reliable, contextual, and useful.
Good judgment regarding where logic should reside (dbt, Hex, canonical models).
Ability to set a crisp direction and quality bar while giving strong people room to execute.
Willingness to operate at the managerial and system level while remaining able to inspect actual work.
Ability to challenge senior leaders on misleading or poorly defined KPIs and work with them to build better measures.
Experience establishing monitoring, data-quality checks, ownership, and incident processes for business-critical data.
Taking responsibility for outcomes when issues arise, not just identifying technical faults.

Nice To Haves

Experience helping a data function keep pace with extreme company growth.
Experience operating Redshift or a similar cloud data warehouse at meaningful scale.
Experience using Hex or another modern collaborative analytics platform and enabling self-service without creating competing truths.
Deep experience with acquisition, activation, retention, churn, revenue, cohorts, and unit economics in a consumer or subscription business.
Experience building data systems supporting multiple brands, countries, and/or products without creating separate data universes.
Experience considering how product and operational data can become traceable, auditable evidence for regulators or other high-stakes external stakeholders.
A strong ability to identify exceptional data talent and build a lean, high-performing team.
Ability to move between different types of tasks, from executive dashboards to architecture discussions to vendor escalations to KPI debates.

Responsibilities

Own the data stack end to end, including pipelines, dbt layer, and self-serve reporting.
Set the technical direction for the data function.
Develop the data team, including managing the existing Analytics Engineering Lead and hiring additional Data Engineers.
Govern company metrics, ensuring precise and canonical definitions.
Ensure leaders can confidently use data for decision-making.
Establish clear contracts for how product data enters the warehouse.
Build the evidence layer required to speak credibly with regulators about clinical and AI systems.
Deeply understand key metrics across growth, activation, retention, revenue, product, clinical operations, CX, and Ops.
Ensure pipelines, models, and dashboards are reliable.
Maintain a clean, documented, and scalable dbt layer.
Define standards for staging, intermediate, and marts models in dbt.
Own the definitions behind core company metrics.
Make Hex AI the best interface for understanding the business.
Ensure the data warehouse and pipelines are fast, reliable, and cost-efficient.
Establish how AIOS designs, instruments, and evaluates experiments.
Partner with Engineering on source schemas, product events, data contracts, and backfills.
Act as a key business partner to functional leads, understanding their domains and challenging KPIs.
Design data foundations for demonstrating the quality, safety, and performance of clinical and AI systems to regulators.
Own relationships with data vendors like Hex.
Move important business logic out of ad hoc dashboards into governed models.
Be the DRI for data across all AIOS brands and markets, including ingestion, Redshift, dbt, Hex, product-event tracking, integrations, data quality, monitoring, and access.
Define the evolution of the data platform architecture.
Establish performance, freshness, monitoring, incident response, and cost expectations for the data warehouse and pipelines.
Ensure experiment results are statistically sound and prevent drawing conclusions from weak evidence.
Ensure product changes flow predictably into trustworthy analytical data.
Reach outcomes when models are wrong, dashboards are stale, or vendors are failing.