Staff Data Scientist (Grant)

• You’d be the fourth Data Scientist focused on the Grant business, and part of a larger Kikoff wide Data team. You’ll take on a product area within Grant as its data lead, working day to day with the leads for that area, and you’ll bring a staff-level view to the Grant-wide conversations on where the business goes next
• Two things we’re asking of this hire beyond the product area. First, help set technical direction and best practices for data science across Kikoff, not just Grant
• Second, help define how we work as AI agents become a core part of the analysis loop, from exploration to pipelines to experiment readouts. We’re actively rebuilding our workflow around this and want someone who has opinions
• Lead the data work for a Grant product area: set the questions worth answering, build the evidence, and drive what happens next. Sometimes the right call is not to act on a finding, and you’ll make that case too
• Define and maintain the measurement system for your product area (acquisition, activation, usage, repayment, losses, unit economics) and contribute to the Grant-wide measurement framework alongside the other data scientists on the team
• Own product experimentation for your area: design, guardrails, analysis, and the recommendation on rollouts and policy changes (eligibility, limits, pricing), including changes where clean randomization isn’t available
• Build and evaluate models where they’re the right tool: proof-of-concept and challenger models, offline evaluation, threshold and policy decisions, and production monitoring with engineering. The ML platform and production model lifecycle sit with our ML engineering team; how data and engineering divide that work is still evolving and you’ll have a voice in it
• Partner with Grant’s business lead and the area leads on roadmap and objectives: which bets, what a win looks like, and what we’d need to see to stop
• As a staff data scientist, contribute to technical direction and best practices for data science across Kikoff, not just Grant: how we do experimentation, how we use AI tooling, how we review each other’s work

Benefits

• Health Benefits
• Flexible PTO Policy
• Kickoff Paid Holidays
• Office Lunch & Dinners
• Commuter Benefits
• Commuter Flexible Savings
• 401k Matching
• Fitness Benefit Policy
• Experience partnering with product, engineering, and marketing peers across the whole arc of the work: strategy, goal setting, approach, and execution, not just the analysis at the end
• AI tools are a core part of your daily analytical work and you can show how they changed the speed and quality of what you ship
• Hands-on with production-quality SQL and Python. You build pipelines, analyses, and models yourself
• Designed and run experimentation programs, including changes where clean randomization wasn’t available. Comfortable with quasi-experimental and causal inference methods, and clear about their limits
• A track record of defining metrics from scratch and getting a team to run on them
• You drive decisions with data in front of executive audiences, including when the data doesn’t support the plan
• Experience building or working closely with models that drive decisions in a product, in any domain: ranking, fraud, forecasting, personalization, underwriting, detection. We care about the judgment, not the vertical
• Consumer fintech experience, especially products that expand access for un- and under-banked customers
• Built an experimentation or causal inference practice in an org that didn’t have one
• Have taken a model from proof of concept to production, or shipped test and challenger models that changed a product decision
• Prior staff or tech-lead scope: set direction for other ICs and owned a domain’s data strategy
• 8+ years in data science or analytics. Strong senior candidates will be considered for a Senior Data Scientist version of this role