Kikoff
Senior Data Scientist
San Francisco · Senior
Sponsorship not specified$226k-$254kDetected 13 days ago
SQLData EngineeringData ScienceLLMsAgentic AIA/B TestingForecastingValuation5G/LTECommunicationUnderwriting
Stay score
odds of building a lasting career here
61Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role90
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70
Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.
Lottery odds assume a STEM candidate.
Personalize to your clock →H-1B wage level
the lottery is wage-weighted — each level is one more entry
Level I$110,3861 entry
Level II$142,4382 entries
Level III$174,5123 entries
Level IV$206,5654 entries
This range already reaches Level IV — the maximum four lottery entries.
DOL prevailing wage, 2026-27 wage year · Data Scientists (15-2051) · San Francisco-Oakland-Fremont, CA. Wage level is derived by USCIS from the offered wage, occupation and worksite; the occupation shown is inferred from the job title.
Employer immigration record
from this employer's Department of Labor filings
Files H-1B transfers
8 transfer filings in the last year, covering 8 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.
Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.
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About the role
- 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: how we do experimentation, how we evaluate AI products, how we review each other's work.
- Second, help define how we work as AI agents become a core part of the analysis loop, from exploration to pipelines to experiment readouts.
Responsibilities
- Lead the data work for a core Kikoff 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 area across the whole customer journey (activation, engagement, credit outcomes, retention, revenue, unit economics), and contribute to the Kikoff-wide measurement framework alongside the other data scientists on the team.
- Where acquisition intersects with what you own, you'll work it jointly with Marketing DS rather than around them.
- Own product experimentation for your area: design, guardrails, analysis, and the recommendation on rollouts, including the cases where a holdout isn't clean or the effect you care about (a customer's score) moves on its own schedule.
- For AI product surfaces, own evaluation: decide what "good" means in checkable terms, build and validate automated scorers against human judgment, and turn what you find in real conversations into regression tests so the product can't quietly get worse.
- Build and evaluate models where they're the right tool: proof-of-concept and challenger models, offline evaluation, threshold decisions, and production monitoring with engineering. Production model lifecycle sits with engineering today
- Partner with product, engineering, design, and lifecycle marketing leads on roadmap and objectives: which bets, what a win looks like, and what we'd need to see to stop.
- Lead the data work for a core Kikoff product area: set the questions worth answering, build the evidence, and drive what happens next.
- Build and evaluate models where they're the right tool: proof-of-concept and challenger models, offline evaluation, threshold decisions, and production monitoring with engineering.
- With record revenue growth in 2025 and a unicorn valuation, we've built a suite of products that help millions of people build credit, access liquidity, and save money.
Requirements
- 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, including for products where success was hard to pin down.
Nice to have
- 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.
- Have mentored, onboarded, or managed the work of other data scientists.
- A note on applying: We know great candidates sometimes hold back if they don't meet every qualification listed.
- If you're excited about this role and believe you can do the work, we encourage you to apply.
- Equal Employment Opportunity Statement
- Kikoff Inc. is an equal opportunity employer.
- Please reference the following for more information.
Compensation
- $226,000 - $254,000 USD
Company info
- We're looking for capability and trajectory, not a perfect checklist match.
- Kikoff is a profitable, pre-IPO fintech company on a mission to empower everyone to achieve financial security.
- 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.
Equal opportunity
- 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.
Visa & Work Authorization
- in, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class
This listing is sourced directly from Kikoff's careers page and normalized into a canonical job model.