Staff Data Scientist, Grant

About Grant

Grant is Kikoff's fastest growing business line. It started with earned wage access, solving the most common and most painful problem in consumer finance: short-term liquidity. Gas to get to work, an unexpected bill, groceries before the next paycheck. We give people fast, fair access to earned wages without the fees the industry has normalized, and we do it with a profitable business model.

Since launching to the public at the start of 2025, Grant has grown from thousands to 900k+ active subscribers and has disbursed and recollected over $325M in cash advances. EWA is the core, and several new products are underway on top of it.

Grant runs as its own business inside Kikoff, with a business lead and dedicated product, engineering, design, and marketing leads.

About the Grant data science team

Our job is to make sure every Grant product does three things: makes a clear and compelling promise to the customer, delivers on that promise reliably over time, and turns that durable value into a business healthy enough to fund the next product, in a way customers would agree is fair. Every metric we define, experiment we run, and model we build should trace back to one of those three.

We're a team of three data scientists within Kikoff's Data organization, embedded full time with Grant. Each of us leads the data work for a product area and all of us take part in Grant-level roadmap and objective setting with Grant's business lead and the product, engineering, design, and marketing leads.

About the role

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.

What you'll do

• 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.

Minimum qualifications

• 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.
• A track record of defining metrics from scratch and getting a team to run on them.
• 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.
• Hands-on with production-quality SQL and Python. You build pipelines, analyses, and models yourself.
• 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.
• 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.
• You drive decisions with data in front of executive audiences, including when the data doesn't support the plan.

Preferred qualifications

• 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.