Head of Data

As Head of Data, you will own the data strategy and execution across the organization. You will set the technical direction, build the management structure, and be a direct partner to senior leadership on the decisions that matter most to the business.

This is not a hands-off role. You will be close to the models, close to the data, and close to the business.

• Manage, coach, and develop a team of 15+ data scientists and engineers, including direct reports who are themselves managing ICs
• Own the hiring plan to grow the team to ~25 by end of year, including hiring and developing managers who can each own a sub-domain of the work
• Set a high bar for technical rigor, ownership, and business impact across the team

• Define and execute the data science roadmap across credit risk, growth, product analytics, and fraud
• Own the full ML model lifecycle: scoping, development, validation, deployment, monitoring, and governance
• Maintain best practices for experimentation design, causal inference, A/B testing, and statistical analysis across the company
• Ensure data science output is production-grade, well-documented, and auditable.

• Translate complex business problems into actionable data science workstreams
• Partner with Product, Engineering, Marketing, Risk, and Finance leadership as a peer — not a service function
• Present findings and recommendations directly to executive leadership

• Oversee data engineering and internal pipeline work that supports model development and analytics
• Partner with Engineering to evolve the data infrastructure as the company scales
• Drive data quality, reliability, and governance standards across the organization

• 10+ years of experience in data science, applied ML, or product analytics — with substantial time in consumer-facing, data-intensive businesses
• 7+ years leading data science teams, including direct experience managing managers — you have built an org that does not depend on you for every decision
• Demonstrated experience scaling a data team through a growth phase: you have hired managers, and you know how to grow a team without losing quality or culture
• Deep technical fluency in ML modeling, statistical inference, causal analysis, and experimentation — you can engage at the model level when it matters
• A track record of translating data science work into product and business decisions that moved the needle at the executive level
• The ability to make your team better every day: through clarity of goals, honest feedback, and building trust with strong practitioners
• Strong command of Python or R, SQL, and modern cloud data platforms
• Experience owning model governance and the full model lifecycle in a production environment

• Experience in consumer credit, risk underwriting, or fraud modeling
• Familiarity with regulatory and model governance expectations in consumer lending
• Experience with BI and data tools such as Metabase, Looker, dbt, Airflow, or Fivetran
• Experience managing data engineering teams or working closely with data infrastructure

Scrappy. We move fast and build what we need, not what sounds impressive. We don't cut corners when they matter, and we don't over-engineer when they don't.

Risk-oriented. We make tradeoffs deliberately. A mature team knows the difference between a bet worth taking and one that isn't.

Data-obsessed. We all look at data, pull it, and believe that understanding the mechanics yields real insight. You will be surrounded by people who think this way.

Humble. We know this journey has required timing, circumstance, and the right people showing up. We are grateful, and we don't take it for granted.