UX & Predictive Experience Lead Researcher
New
S
SafeliteDigital product research
Source API remote eligibility restrictions: United StatesFull-TimeLead
Salary not disclosed
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Job Details
- Experience
- 8+ years across UX research, behavioral science, data science, experimentation, or related disciplines, with significant experience shaping digital product decisions.
- Required Skills
- PythonSQLBehavioral scienceR
Requirements
- Bachelor's degree or equivalent work experience.
- 8+ years across UX research, behavioral science, data science, experimentation, or related disciplines, with significant experience shaping digital product decisions.
- Demonstrated experience building predictive, behavioral, simulation, or decision-support methods using first-party data.
- Strong applied statistics and experimentation foundation, including causal inference, experimental design, and working explicitly with uncertainty.
- Working fluency in Python or R and SQL.
- Hands-on qualitative research experience and ability to connect behavioral evidence with customer motivation and context.
- Demonstrated fluency using AI across research, modeling, simulation, analysis, and prototyping.
- Experience operating in continuous discovery and delivery environments and using evidence to shape what gets built next.
- Experience with digital analytics and experimentation platforms such as Quantum Metric, Amplitude, Adobe, Optimizely, or similar.
- Ability to make sophisticated methods and evidence understandable and actionable for senior product and business leaders.
Responsibilities
- Build a learning system that helps teams reduce uncertainty earlier in the product development cycle.
- Combine behavioral data, predictive methods, simulation, AI, experimentation, and research based on the decision to be made.
- Develop models and simulations using first-party behavioral data to anticipate customer response to potential experience changes.
- Create synthetic customer models to explore scenarios, challenge assumptions, and identify promising directions.
- Validate predictive methods against observed customer behavior and known outcomes, and define confidence levels and limitations.
- Combine behavioral signals with qualitative research to understand customer behavior, motivation, and context.
- Design research and experiments around the uncertainty that matters most to a decision.
- Translate complex evidence into clear implications for product and design teams.
- Create tools and ways of working that help teams use predictive insight without needing to become modeling experts.
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