Predictive UX Research & Experience Lead

City Auto Glass, inc.

Kentucky

Hybrid

USD 120,000 - 180,000

Full time

14 days+

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Benefits offered by this job

Weekly pay & bonus

Benefits >$10k (401k, medical)

Tuition reimbursement

Job summary

Safelite is seeking a UX & Predictive Experience Lead Researcher to accelerate learning across our digital product teams. You will combine first-party data, predictive modeling, AI, experimentation, and direct customer research to help teams learn earlier and invest with greater confidence.

Reporting to the Director of Product Design, you will lead this capability as a senior IC, collaborating with research, design, analytics, and data science to translate evidence into actionable product

Qualifications

7-9 years across UX research, behavioral science, data science, or related disciplines, with significant experience shaping digital product decisions.
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 the 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

Increase learning velocity
Build a learning system that helps teams reduce uncertainty earlier in the product development cycle.
Use behavioral data, predictive methods, simulation, AI, experimentation, and research together, choosing the method based on the decision we need to make.
Create ways to explore multiple hypotheses and experience directions before committing significant time and investment.
Help teams distinguish between what we know, what the evidence suggests, and what still needs to be learned
Build predictive experience capability
Develop models and simulations that use first‑party behavioral data to anticipate customer response to potential experience changes.
Create synthetic customer models that allow teams to explore scenarios, challenge assumptions, and identify promising directions earlier.
Use predictive signals to inform prioritization and identify where additional discovery or experimentation will create the most value.
Advance the methods as new data, AI capabilities, and approaches become available.
Establish confidence in the signal
Validate predictive methods against observed customer behavior and known outcomes.
Define confidence levels, limitations, and appropriate use so teams understand how to act on the output.
Investigate divergence between predicted and observed behavior as a source of learning, not simply model error.
Continuously improve the models and methods as new evidence is available.
Connect signals to human understanding
Combine behavioral signals with qualitative research to understand both what customers are likely to do and why.
Design research and experiments around the uncertainty that matters most to the decision.
Use direct customer research where context, motivation, unmet needs, or emerging behavior cannot be inferred reliably from existing data.
Feed new customer understanding back into the broader learning system.
Turn learning into better decisions
Translate complex evidence into clear implications for product and design teams.
Create tools and ways of working that allow teams to use predictive insight without needing to become modeling experts.
Bring a point of view on where we should explore, experiment, invest, or change direction based on the strength of the evidence.
Raise the organization’s fluency in evidence-based decision making, experimentation, predictive methods, and AI.
Performs other duties as assigned
Complies with all policies and standards
Bachelor's Degree or equivalent work experience Required