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Salary: USD 265,000 - 306,000 / annual
Kikoff is a profitable, pre-IPO fintech company on a mission to empower financial security at scale. Grant is Kikoff's fastest-growing business line, launched in early 2025 as an earned wage access (EWA) platform. Since launch, Grant has grown to 900k+ active subscribers and disbursed over $325M in cash advances, solving short-term liquidity needs for underbanked consumers without predatory fees.
You will join Grant's dedicated data science team as the fourth data scientist, embedded full-time within the business unit. You'll lead data strategy for a specific product area within Grant while contributing staff-level technical direction across Kikoff's entire data organization.
**Core Responsibilities:**
- Lead data work for a Grant product area: define key questions, build evidence, and drive decisions on acquisition, activation, usage, repayment, losses, and unit economics.
- Own the measurement system for your product area and contribute to Grant-wide measurement frameworks alongside peer data scientists.
- Design and execute product experimentation programs, including quasi-experimental and causal inference methods where clean randomization isn't available. Make recommendations on rollouts, eligibility changes, limits, and pricing.
- Build and evaluate models that drive product decisions (proof-of-concept, challenger models, offline evaluation, threshold/policy decisions, production monitoring). Collaborate with ML engineering on model lifecycle and production deployment.
- Partner with Grant's business lead and product/engineering/design/marketing leads on roadmap planning, objective setting, and defining success metrics.
- Set technical direction and best practices for data science across Kikoff: experimentation methodology, AI tooling integration, and peer review processes.
- Drive data-informed decisions with executive audiences, including when data contradicts proposed plans.
**Requirements:**
- Proven experience partnering across product, engineering, and marketing teams throughout the full project lifecycle (strategy, goal-setting, approach, execution).
- Track record of defining metrics from scratch and establishing them as organizational standards.
- Designed and executed experimentation programs with experience in quasi-experimental and causal inference methods; clear understanding of their limitations.
- Production-level proficiency in SQL and Python; hands-on building of pipelines, analyses, and models.
- Experience building or closely collaborating on models that drive product decisions (ranking, fraud, forecasting, personalization, underwriting, detection).
- Daily use of AI tools in analytical work with demonstrated impact on speed and quality of deliverables.
- Ability to present data-driven recommendations to executives, including cases where data contradicts proposed direction.
- Preferred: consumer fintech experience (especially products for underbanked populations), built experimentation/causal inference practices in organizations lacking them, shipped models from proof-of-concept to production, prior staff or tech-lead scope setting direction for other ICs, 8+ years in data science/analytics.
