Head of Data Science at Kikoff

San Francisco, California, United States -

Full Time

Start Date

Immediate

Expiry Date

22 Jun, 26

Salary

486000.0

Posted On

24 Mar, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Science Strategy, Team Management, Hiring, Technical Rigor, ML Model Lifecycle, A/B Testing, Causal Inference, Python, R, SQL, Cloud Data Platforms, Model Governance, Credit Risk, Growth, Product Analytics, Fraud

Industry

Software Development

Description

Kikoff: A FinTech Unicorn Powering Financial Progress with AI At Kikoff, our mission is to provide radically affordable financial tools to help consumers achieve financial security. We're a profitable, high growth FinTech unicorn serving millions of people, many of whom are building credit or navigating life paycheck to paycheck. With innovative technology and AI, we simplify credit building, reduce debt, and expand access to financial opportunities to those who need them the most. Founded in 2019, Kikoff is headquartered in San Francisco and backed by top-tier VC investors and NBA star Stephen Curry. Why Kikoff: This is a consumer fintech startup, and you will be working with serial entrepreneurs who have built strong consumer brands and innovative products. We value extreme ownership, clear communication, a strong sense of craftsmanship, and the desire to create lasting work and work relationships. Yes, you can build an exciting business AND have real-life real-customer impact. About the Role 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. In This Role, You Will 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 You Have 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 Nice to Have 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 What We're Like 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. Base Range $398,000—$486,000 USD Equal Employment Opportunity Statement Kikoff Inc. is an equal opportunity employer. We are committed to complying with all federal, state, and local laws providing equal employment opportunities and considers qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class. Please reference the following for more information.

Responsibilities

The Head of Data will own the organization's data strategy and execution, setting technical direction, building management structure, and partnering with senior leadership on key business decisions. This role involves leading and growing a team of 15+ data scientists and engineers, defining the data science roadmap across key domains like credit risk and growth, and overseeing the full ML model lifecycle.