Senior UX Researcher, Quantitative

Ondo FinanceFinancial technology

Remote (US)Full-TimeSenior

SalaryCompetitive compensation including but not limited to salary, future token rights, and/or equity (according to your preferences)

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Job Details

Experience: 7+ years of UX or applied research experience, including at least 5 years leading quantitative studies end to end, from study design through recommendation.
Required Skills: PythonR

Have 7+ years of UX or applied research experience, including at least 5 years leading quantitative studies end to end.
Have experience leading quantitative research studies for financial products such as brokerage, trading, banking, payments, wealth, or lending.
Be able to scope ambiguous problems, set priorities, and drive studies to a decision with minimal guidance.
Have deep survey science expertise, including sampling and weighting, instrument design, bias control, and data-quality screening.
Have experience programming in Qualtrics or a similar tool.
Have hands-on experience designing and analyzing MaxDiff and/or conjoint studies.
Apply regression, power analysis, and experimental and quasi-experimental design.
Use R or Python for analysis and visualization.
Have an AI-assisted research setup for tasks such as drafting survey instruments and writing or reviewing analysis code, and rigorously validate its outputs.
Have a track record of quantitative research changing a product or business decision.
Be able to explain uncertainty clearly to executives and non-researchers.
Experience with crypto, onchain, or tokenized-asset products is a nice to have.

Own research intake and the quantitative research roadmap across product, design, go-to-market, and leadership.
Turn ambiguous research requests into answerable questions and prioritize studies based on how much they reduce risk.
Use MaxDiff and conjoint to assess which features, benefits, and messages drive decisions.
Design pre-launch experiments, set decision rules before fielding, and pretest instruments with cognitive interviews.
Adapt or develop validated scales for trust, perceived security, and comprehension, and track them across products and markets.
Create statistically validated, sized user segments.
Design sampling, translation, and fraud controls for a global, crypto-native audience.
Partner with data science to link survey data with product data.
Design human evaluations of AI-assisted finance features, including rubrics and rater reliability.
Present findings and recommendations to leadership, including effect sizes, confidence, and what could change the recommendation.

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