Head of Data, Kikoff

• We value extreme ownership, clear communication, a strong sense of craftsmanship, and the desire to create lasting work and work relationships
• 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

Desirable

• This is a consumer fintech startup, and you will be working with serial entrepreneurs who have built strong consumer brands and innovative products
• 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
• Lead and Grow the Team:
• 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
• Own the Data Science Strategy:
• 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
• Drive Business Impact:
• 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
• Own the Data Foundation:
• 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