SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Salary: USD 226,000 - 254,000 / annual
Kikoff is a profitable, pre-IPO fintech company on a mission to empower financial security at scale. With record revenue growth in 2025 and a unicorn valuation, the company has built a suite of products helping millions of people build credit, access liquidity, and save money.
You will join a data organization of roughly 20 people across product data science, marketing data science, and data engineering. In this role, you'll take on a product area within core Kikoff as its data lead, working day-to-day with product, engineering, design, and lifecycle marketing leads. You'll sit in Kikoff-wide conversations on roadmap and objectives.
Key responsibilities include:
- Lead data work for a core product area: set the questions worth answering, build evidence, and drive decisions. You'll define and maintain the measurement system across the customer journey (activation, engagement, credit outcomes, retention, revenue, unit economics).
- Own product experimentation: design, guardrails, analysis, and rollout recommendations, including quasi-experimental cases where clean randomization isn't available.
- For AI product surfaces, own evaluation: define what "good" means, build and validate automated scorers against human judgment, and create regression tests to prevent silent degradation.
- Build and evaluate models where appropriate: proof-of-concept, challenger models, offline evaluation, threshold decisions, and production monitoring with engineering.
- Partner with cross-functional leads on roadmap and objectives: which bets to pursue, what success looks like, and stopping criteria.
- Raise the bar for peers: review work, onboard teammates, and mentor early-career data scientists.
- Help set technical direction and best practices for data science across Kikoff: experimentation methodology, AI product evaluation, and peer review standards.
- Define how the organization works as AI agents become core to the analysis loop, from exploration to pipelines to experiment readouts.
Requirements:
- Experience partnering with product, engineering, and marketing peers across the full arc of work: strategy, goal setting, approach, and execution.
- Track record of defining metrics from scratch and getting teams to run on them, including for products where success was hard to pin down.
- Designed and ran experimentation programs, including cases without clean randomization. Comfortable with quasi-experimental and causal inference methods and their limits.
- Hands-on with production-quality SQL and Python. You build pipelines, analyses, and models yourself.
- Experience building or working closely with models that drive product decisions (ranking, fraud, forecasting, personalization, underwriting, detection, LLM applications).
- AI tools are a core part of your daily analytical work; you can demonstrate how they changed the speed and quality of what you ship.
- Drive decisions with data in front of senior audiences, including when data doesn't support the plan.
Preferred qualifications:
- Built or ran an evaluation program for an LLM-based product: judge design, validation against human labels, test-case construction from real failures.
- Consumer fintech experience, especially products expanding access for un- and under-banked customers.
- Built an experimentation or causal inference practice in an organization that didn't have one.
- Taken a model from proof of concept to production, or shipped test and challenger models that changed a product decision.
- Mentored, onboarded, or managed the work of other data scientists.
