Head of Data

Company: r.Potential

Location: San Francisco Bay Area (Hybrid 2-3 days per week in office)

Reports to: VP of Engineering

Team: Data Science, Data Engineering, Data Platform

The Role

Data is the product at r.Potential. Our measurement and governance layer is only as good as the data underneath it and the judgment applied to it, which makes this as much a product role as a technical one.

We are looking for a Head of Data to own that end to end: the strategy for what data we hold, what data we go acquire, what we build with it, and how we expose it. You will report to the Chief Operating Officer and work directly with the CEO, Engineering, and Product on decisions that shape what the company sells - not only the systems underneath it.

Three things define the job:

• Data strategy. A clear point of view on where our data assets should be in two years, what has to be true to get there, and what we sequence first.
• External data.
  Third-party and partner data sources are central to what we can build. Assessing them, understanding what each one permits, and deciding how they fit together is a core part of this role.
• Data as an interface.
  Our data has to be consumable by systems, not only by people. That makes this function the owner of the contracts other teams and customers build against, not just internal tables.

You will lead an existing data team with additional headcount in flight, and you will stay hands-on as that team grows. The right person is credible with data scientists, data engineers, and infrastructure partners - and equally credible in a product review or in front of an enterprise buyer asking why our numbers can be trusted.

What You Will Do

Own r.Potential's data strategy: a defensible point of view on which data assets matter, what to build, what to acquire, and in what order.
• Define and drive the data product roadmap with Product - what our data makes possible for customers, and which capabilities we take to market.
• Own our approach to external data: evaluate third-party and partner sources, determine how they combine with our own, and establish what each source permits - usage rights, derived-work rights, and downstream exposure - before the product depends on it.
• Establish provenance, lineage, and entitlement discipline, so we can state at any moment where a data point came from and what we are permitted to do with it.
• Own the data behind our measurement claims: metric definitions, methodology, and the analytical rigor that makes our outputs defensible to enterprise buyers and their auditors.
• Shape our external data interfaces alongside Engineering, including schema design, versioning, deprecation, documentation, and what is safe and permitted to expose.
• Lead, hire, and develop the data team across data science, data engineering, and data platform disciplines.
• Own the architecture and scalability of the end-to-end data platform, currently Databricks-centered, setting practices for pipelines, governance, performance, cost management, and reliability - directly at first, through the team as it grows.
• Guide production machine learning and analytics work: model development, deployment, monitoring, experimentation, and data quality.
• Ensure our data architecture and operating practices support ISO 27001 and SOC 2 obligations - access controls, lineage, documentation, monitoring, auditability - in partnership with Security.
• Partner with Engineering, Product, Security, and executive leadership to translate company priorities into data initiatives, and to bring forward data opportunities leadership would not have thought to ask for.
• Foster a culture of responsible AI development that augments human work.

What We Are Looking For

Must Haves
• 10+ years in data science, machine learning, or data engineering, including 4+ years leading technical teams.
• Demonstrated ownership of a data strategy: you have decided what data an organization should hold and acquire and been accountable for the outcome, rather than executing a roadmap handed to you.
• A track record of building data products - data or analytics capabilities that customers used and paid for, not exclusively internal analytics and reporting.
• Experience working with third-party or partner datasets, including the practical realities of licensing constraints, usage rights, and provenance.
• Strong hands-on technical foundation: Python, SQL, production machine learning workflows, statistical modeling, and scalable data architecture. You can review a model or a pipeline design and be right.
• Experience designing data models, distributed data systems, and reliable pipelines in a modern lakehouse or warehouse environment; able to own a Databricks-based platform or ramp quickly to it.
• Product instinct and the communication to match: you can hold your own with Product and Engineering on what to build, and with enterprise customers on why our numbers are trustworthy.
• Ability to operate in a fast-paced, ambiguous startup environment, comfortable being hands-on as well as directing the work, with strong communication and stakeholder management skills.
• Based in the San Francisco Bay Area and able to work hybrid, with alignment on our belief that AI should be deployed responsibly to augment human work.

Nice to Haves
• Experience with externally consumed data APIs or programmatic data interfaces: schema versioning, SLAs, and developer documentation.
• Experience with labor-market, HR, workforce, or organizational data.
• Deep Databricks expertise, including workspace design, governance, and cost management.
• Familiarity with Terraform and infrastructure as code, and with cloud platforms - Microsoft Azure in particular.
• Familiarity with data governance practices that support ISO 27001 and SOC 2 compliance.
• Experience with CI/CD, MLOps frameworks, and modern data orchestration tooling; NoSQL databases, messaging systems, and streaming technologies.
• Advanced degree in a quantitative field, or prior experience in a startup or high-growth environment.

Our Tech Stack
• Databricks
• PostgreSQL
• Microsoft Azure
• Python, Spark, SQL
• REST APIs and microservices

Why Join r.Potential
• Serious backing. The Adecco Group and Salesforce are behind us, and we work on labor-market data at enterprise scale.
• Data is the product. This role shapes what the company sells, not only how it runs.
• Build the function. An established team, active headcount, and the mandate to define how data works here.
• A real problem. Work at the intersection of AI, measurement, and the future of work, on something enterprises are actively struggling with today.