UX & Predictive Experience Lead Researcher

📍 Job Overview

Job Title: UX & Predictive Experience Lead Researcher

Company: Safelite

Location: OH Mobile Pro & Sales, United States of America (Remote OK)

Job Type: Full time

Category: User Experience Research & Data Science

Date Posted: September 01, 2026

Experience Level: 10+ years

Remote Status: Remote OK

🚀 Role Summary

• Drive learning velocity and reduce uncertainty across the digital product organization by integrating diverse data sources and research methodologies.

• Build and deploy predictive experience capabilities, leveraging first-party behavioral data, AI, and simulation to anticipate customer responses to experience changes.

• Establish robust validation processes for predictive methods, ensuring confidence in signals and understanding their limitations for effective decision-making.

• Bridge the gap between complex data insights and actionable product strategies, translating evidence into clear implications for product and design teams.

• Elevate the organization's proficiency in evidence-based decision-making, experimentation, predictive methods, and AI applications within product development.

📝 Enhancement Note: This role is positioned as a senior individual contributor focused on building a new "learning system" capability within Safelite's digital product organization. The emphasis is on a proactive, integrated approach to research and prediction rather than a sequential study model. The "Predictive Experience" aspect suggests a forward-looking role, aiming to anticipate user needs and behaviors to inform strategy and product development.

📈 Primary Responsibilities

• Lead the development and implementation of a comprehensive learning system designed to accelerate discovery and reduce uncertainty in the product development lifecycle.

• Design and execute research strategies that combine behavioral data analysis, predictive modeling, AI, experimentation, and qualitative customer insights to inform product decisions.

• Build and deploy predictive models and simulations utilizing first-party behavioral data to forecast customer reactions to potential digital experience enhancements.

• Develop synthetic customer models to enable exploration of various hypotheses and experience directions, thereby optimizing investment and resource allocation.

• Establish clear confidence levels and limitations for predictive insights, ensuring stakeholders understand how to interpret and act upon the generated evidence.

• Investigate discrepancies between predicted and observed customer behavior as a critical source of continuous learning and model refinement.

• Integrate qualitative research methods with behavioral signals to gain a holistic understanding of customer motivations and contextual factors influencing behavior.

• Translate complex quantitative and qualitative findings into clear, actionable recommendations for product managers, designers, and business leaders.

• Champion and evangelize evidence-based decision-making, experimentation best practices, and the strategic application of AI and predictive methods across the digital product organization.

• Foster a culture of continuous learning and data-driven innovation within cross-functional product teams.

📝 Enhancement Note: The responsibilities highlight a blend of advanced data science, UX research, and strategic product thinking. The role requires not only technical proficiency in data analysis and modeling but also the ability to translate complex findings into actionable insights for non-technical stakeholders. The emphasis on building a "learning system" and "predictive experience capability" indicates a strategic mandate to fundamentally improve how Safelite understands and anticipates customer behavior to drive product innovation.

🎓 Skills & Qualifications

Education: Bachelor's Degree or equivalent work experience Required.

Experience: 8+ years across UX research, behavioral science, data science, experimentation, or related disciplines, with significant experience shaping digital product decisions.

Required Skills:

• 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 for data manipulation, analysis, and modeling.

• 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, using evidence to shape product roadmaps and priorities.

• Experience with digital analytics and experimentation platforms such as Quantum Metric, Amplitude, Adobe Analytics, Optimizely, or similar.

• Exceptional ability to make sophisticated methods and evidence understandable and actionable for senior product and business leaders. Preferred Skills:

• Experience with advanced AI/ML techniques relevant to customer behavior prediction and simulation.

• Familiarity with data visualization tools for communicating complex insights.

• Experience in the automotive or service industry, understanding customer journey dynamics in such contexts.

• A Master's or Ph.D. in a quantitative field such as Computer Science, Statistics, Psychology, or a related discipline.

📝 Enhancement Note: The experience requirement is explicitly stated as 8+ years, but the "AI Experience Level" derived as "10+" suggests a strong preference for candidates with extensive experience, aligning with a "Lead" researcher role. The technical skills emphasize a strong quantitative foundation (Python/R, SQL, statistics, experimentation) combined with AI/ML expertise and practical experience with common digital analytics and experimentation platforms. The ability to communicate complex findings to leadership is a critical soft skill emphasized.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Showcase examples of building and deploying predictive models or simulations using real-world data.

• Include case studies demonstrating how research insights and predictive methods have directly influenced product strategy or design decisions, leading to measurable improvements.

• Present projects that illustrate the integration of qualitative research with quantitative behavioral data and predictive analytics.

• Provide examples of how complex data and model outputs were translated into clear, actionable recommendations for product teams.

• Demonstrate experience in experimental design and analysis, including causal inference and uncertainty quantification. Process Documentation:

• Detail methodologies used for validating predictive models against observed outcomes and establishing confidence levels.

• Document approaches for integrating predictive insights into continuous discovery and delivery workflows.

• Illustrate how qualitative research was used to contextualize and explain behavioral patterns identified through data analysis.

• Outline strategies for communicating complex findings and recommendations to diverse stakeholder groups, including senior leadership.

• Showcase experience with defining and tracking key metrics related to learning velocity and product decision impact.

📝 Enhancement Note: For a role focused on building "predictive experience capabilities" and a "learning system," a portfolio is crucial. It should not only showcase technical skills but also the ability to translate complex analytical work into tangible business impact. Candidates should be prepared to discuss their process for hypothesis generation, model building, validation, and communicating findings. The emphasis on reducing uncertainty and informing decisions means portfolio pieces should clearly articulate the problem, the approach, the insights, and the resulting actions and outcomes.

💵 Compensation & Benefits

Salary Range:

Given the "Lead Researcher" title, 8+ years of experience, the specialized skill set (UX research, data science, predictive modeling, AI), and the remote work option in the US, a competitive salary range for this role in Ohio (or nationally for remote) would typically fall between $130,000 - $180,000 per year. This estimate is based on industry benchmarks for senior-level research and data science roles in the technology and digital product space, considering factors like advanced analytics, AI application, and strategic influence.

Benefits:

• Competitive weekly pay and bonus opportunities.

• Comprehensive 401(k) plan with company matching, fostering long-term financial planning.

• Medical coverage plans, customized to suit individual and family needs.

• Generous Paid Time Off (PTO) programs, company holidays, and paid volunteer days, promoting work-life balance.

• Up to $5,250 annually in tuition reimbursement, supporting continuous learning and professional development. Working Hours:

• Standard full-time hours, estimated at 40 hours per week.

• The role is primarily remote, offering flexibility in daily work structure, though core collaboration hours will likely be expected.

📝 Enhancement Note: While a specific salary range isn't provided in the raw data, industry standards for a "Lead Researcher" with 8+ years of experience in a specialized field like predictive analytics and AI, particularly in a remote-friendly capacity, suggest a significant compensation package. The provided benefits are standard for a large organization and include strong support for professional development and work-life balance. The "weekly pay" mention is unusual for salaried roles and might indicate a specific payroll structure or a misunderstanding in the source data; typically, salaried positions are paid bi-weekly or monthly.

🎯 Team & Company Context

🏢 Company Culture

Industry: Automotive aftermarket, specifically auto glass repair and replacement. Safelite is a leader in its segment, emphasizing customer experience and digital transformation.

Company Size: Large enterprise; Safelite is a significant player in its industry, likely employing thousands of individuals across various functions and locations. This scale implies structured processes and opportunities for broad impact.

Founded: Safelite was founded in 1937, with a long history of service. Its evolution into a digital-first company suggests a culture that values both tradition and innovation.

Team Structure:

• The role reports to the Director of Product Design, indicating a close alignment with the product development and user experience function.

• Collaboration will be cross-functional, involving research, design, product management, analytics, and data science teams, requiring strong interpersonal and communication skills.

• As a senior individual contributor, the researcher will likely lead initiatives and mentor others rather than directly managing a team. Methodology:

• Emphasis on a "learning system" approach, integrating diverse data sources (behavioral, predictive, AI, qualitative) for faster and more confident decision-making.

• Commitment to experimentation and evidence-based decision-making as core tenets of product development.

• Focus on building predictive capabilities and simulations to explore future scenarios and reduce uncertainty.

Company Website: [Safelite Careers URL: https://belron.wd3.myworkdayjobs.com/Safelite_Careers] (Directly linking to the career portal is more relevant here than the main company site for an applicant).

📝 Enhancement Note: Safelite's positioning as a leader in auto glass services, coupled with its ambition in digital transformation, suggests a company culture that is customer-centric and increasingly data-driven. The role's placement within Product Design underscores the importance of user experience and data in shaping digital products. The company's long history implies stability, while its focus on digital innovation suggests an evolving and forward-thinking environment.

📈 Career & Growth Analysis

Operations Career Level: This is a senior individual contributor role, equivalent to a Principal or Lead Researcher/Scientist. It signifies a high level of expertise and the ability to define and drive strategic initiatives independently. The focus is on deep specialization and impact through technical leadership and strategic influence rather than people management.

Reporting Structure: Reporting to the Director of Product Design places the role at a critical intersection of user understanding and product strategy. This offers visibility and the opportunity to influence high-level product decisions. The role will likely collaborate closely with Product Managers, UX Designers, Data Scientists, and Analysts.

Operations Impact: The core mandate of this role is to directly impact Safelite's digital product strategy by reducing uncertainty and increasing the confidence with which product teams make decisions. By building predictive capabilities and a robust learning system, this role will enable faster iteration, more effective resource allocation, and ultimately, the development of digital experiences that better meet customer needs and business objectives. This translates to improved customer satisfaction, operational efficiency, and potentially revenue growth.

Growth Opportunities:

• Specialization & Leadership: Deepen expertise in predictive analytics, AI for user behavior, and experimental design, becoming a recognized thought leader within Safelite in these domains. Potential to shape the future of Safelite's data science and UX research practices.

• Strategic Influence: Grow influence over product roadmaps and strategic planning by consistently delivering high-impact insights and recommendations. Potential to advise executive leadership on digital strategy.

• Mentorship: As a senior individual contributor, there will be opportunities to mentor junior researchers and analysts, guiding them in advanced methodologies and strategic thinking.

• Cross-functional Exposure: Gain broad exposure to various aspects of Safelite's digital transformation by working across different product teams and functions.

📝 Enhancement Note: This role is designed for an experienced professional looking to make a significant impact in a specialized area. The growth path is less about traditional management and more about deepening technical expertise, strategic influence, and becoming a key advisor within the organization. The ability to "raise the organization's fluency" also points to a growth opportunity in shaping best practices and knowledge sharing.

🌐 Work Environment

Office Type: Primarily remote, with the possibility of occasional travel to the Safelite Home Office in Columbus, OH, or other designated locations. This offers significant flexibility for employees.

Office Location(s): While the role is remote, the primary headquarters is in Columbus, OH. This means that while daily work is remote, there's a physical hub for potential in-person meetings or team gatherings. Remote work is expected to be performed primarily within the United States.

Workspace Context:

• Flexibility: The remote nature provides autonomy over the work environment, allowing individuals to set up a workspace that optimizes their productivity and well-being.

• Collaboration Tools: Expect a reliance on digital collaboration tools (e.g., Slack, Microsoft Teams, Zoom, Jira, Confluence) for communication, project management, and knowledge sharing.

• Data & Analytics Infrastructure: Access to Safelite's data infrastructure, analytics platforms, and potentially cloud computing resources will be critical for performing research and building models.

Work Schedule:

• Full-time, estimated at 40 hours per week.

• The remote arrangement likely allows for some flexibility in daily scheduling, but adherence to core business hours and team meeting schedules will be essential for effective collaboration.

📝 Enhancement Note: The "OH Mobile Pro & Sales" location listed in the raw data, combined with the "Remote OK" status and specific mention of travel to Columbus, OH, suggests a hybrid approach where remote is primary, but occasional on-site presence might be required. This is common for senior roles that need to build relationships and influence within headquarters. The emphasis on remote work indicates a modern approach to talent acquisition, allowing Safelite to tap into a wider pool of expertise.

📄 Application & Portfolio Review Process

Interview Process:

1. Initial Screening: A recruiter or hiring manager will likely conduct an initial phone screen to assess basic qualifications, experience alignment, and cultural fit.

2. Technical Interview(s): Expect one or more interviews focused on technical skills. This may include discussions on statistics, experimental design, Python/R/SQL proficiency, AI/ML concepts, and experience with specific platforms. Case studies or whiteboard exercises related to data analysis and problem-solving are probable.

3. Portfolio Review / Deep Dive: A dedicated session where candidates present their portfolio. This is where you'll walk through specific projects, detailing your approach, methodologies, findings, and the impact of your work. Be prepared to discuss challenges, trade-offs, and how you connected data insights to business decisions.

4. Behavioral / Cross-functional Interview: Interviews with peers and potential collaborators (e.g., Product Managers, Designers, Data Scientists) to assess collaboration style, communication skills, and ability to translate complex technical information for non-technical audiences.

5. Final Interview: Likely with the Director of Product Design or another senior leader to discuss strategic alignment, leadership potential, and overall fit with Safelite's vision.

Portfolio Review Tips:

• Curate Strategically: Select 3-4 projects that best demonstrate your experience in predictive modeling, integrating diverse data sources, and driving product decisions. Prioritize projects with clear metrics and demonstrable impact.

• Structure Your Narrative: For each project, clearly articulate: The Problem, Your Role & Approach, The Data & Methods Used, Key Findings/Insights, The Impact/Outcome, and Lessons Learned.

• Quantify Everything: Where possible, use numbers and metrics to showcase the impact of your work (e.g., "increased conversion by X%", "reduced uncertainty by Y%", "informed decision that saved Z dollars").

• Highlight Predictive & AI Work: Emphasize projects involving predictive modeling, simulation, AI applications, and experimental design. Detail your process for validation and confidence building.

• Showcase Communication Skills: Practice explaining complex technical concepts and findings in a clear, concise, and engaging manner suitable for a mixed audience.

Challenge Preparation:

• Data Analysis & Interpretation: Be ready to analyze a dataset or a hypothetical scenario and draw actionable insights.

• Experimental Design: Prepare to design an experiment to test a hypothesis related to user behavior or product features.

• Predictive Modeling Scenario: Discuss how you would approach building a predictive model for a given business problem (e.g., predicting customer churn, likelihood to purchase).

• Translating Insights: Practice explaining how to communicate the implications of your findings to stakeholders who may not have a technical background.

📝 Enhancement Note: The interview process is likely rigorous, given the specialized nature of the role. The portfolio review is a critical component, serving as the primary vehicle for demonstrating practical application of skills. Candidates should prepare to articulate their thought process and the strategic value of their work, not just the technical execution.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: Python and/or R for data analysis, modeling, and scripting.

• Database Querying: SQL for data extraction and manipulation from various databases.

• AI/ML Libraries: Familiarity with libraries such as TensorFlow, PyTorch, scikit-learn, Keras for building predictive models and AI applications.

• Data Science & Analytics Platforms: Experience with cloud-based data platforms (e.g., AWS, Azure, GCP) and data science notebooks (e.g., Jupyter).

Analytics & Reporting:

• Digital Analytics Platforms: Quantum Metric, Amplitude, Adobe Analytics, Google Analytics, or similar tools for tracking user behavior and website/app performance.

• Experimentation Platforms: Optimizely, Adobe Target, VWO, or internal A/B testing frameworks for designing and analyzing experiments.

• Business Intelligence Tools: Tableau, Power BI, Looker, or similar for data visualization and dashboard creation to communicate findings to stakeholders.

CRM & Automation:

• CRM Systems: While not explicitly stated as a primary tool for this role, understanding how CRM data (e.g., Salesforce) can be leveraged for behavioral analysis or predictive modeling would be beneficial.

• Integration Tools: Familiarity with how data is ingested and integrated across different platforms is advantageous.

📝 Enhancement Note: The technology stack emphasizes a strong foundation in data science and analytics tools. Proficiency in programming languages (Python/R) and SQL is non-negotiable. The inclusion of specific analytics and experimentation platforms (Quantum Metric, Amplitude, Optimizely) indicates the types of tools Safelite currently uses or intends to use for understanding customer behavior and optimizing digital experiences. Experience with AI/ML libraries is also a key technical requirement.

👥 Team Culture & Values

Operations Values:

• Customer-Centricity: A deep understanding of customer needs and motivations is paramount, driving the development of intuitive and effective digital experiences.

• Data-Driven Decision Making: A commitment to using evidence, including behavioral data, predictive insights, and experimental results, to guide all product and strategic choices.

• Continuous Learning & Improvement: An ethos of constantly seeking new knowledge, refining methodologies, and embracing innovation in research, analytics, and AI.

• Collaboration & Transparency: Open communication and a willingness to share insights, methodologies, and learnings across teams to foster collective growth and efficiency.

• Impact & Accountability: A focus on delivering measurable business outcomes and taking ownership of the research and insights generated.

Collaboration Style:

• Cross-functional Partnership: Working closely with product managers, designers, engineers, and data scientists to ensure research and insights are integrated into the product development lifecycle.

• Evidence-Based Dialogue: Engaging in constructive discussions grounded in data and research findings to challenge assumptions and drive consensus.

• Knowledge Sharing: Proactively sharing learnings, best practices, and new methodologies through presentations, documentation, and informal discussions to elevate the team's collective capabilities.

• Agile Mindset: Adapting research approaches and timelines to fit within agile development cycles, providing timely insights to support ongoing iteration.

📝 Enhancement Note: Safelite's culture, as inferred from the job description and industry context, likely values efficiency, customer focus, and a growing embrace of data and technology. For this role, the emphasis on "learning velocity" and "reducing uncertainty" suggests a culture that rewards proactive problem-solving and a willingness to experiment. The collaborative nature of the role implies that strong interpersonal skills and the ability to influence without direct authority are highly valued.

⚡ Challenges & Growth Opportunities

Challenges:

• Building a Novel Capability: Establishing a new "learning system" and "predictive experience capability" from the ground up requires significant strategic thinking, stakeholder buy-in, and iterative development.

• Data Integration & Quality: Consolidating and ensuring the quality of first-party behavioral data from various sources can be complex.

• Balancing Predictive Methods with Qualitative Insights: Effectively combining sophisticated predictive models with nuanced human understanding from qualitative research to provide a complete picture.

• Stakeholder Education: Educating product teams and leadership on the value and appropriate use of predictive methods, AI, and advanced analytics.

• Rapidly Evolving Technology: Keeping pace with advancements in AI, machine learning, and research methodologies to ensure Safelite remains at the forefront.

Learning & Development Opportunities:

• Deep Dive into AI/ML: Opportunities to expand expertise in applying advanced AI and machine learning techniques to real-world customer behavior problems.

• Strategic Product Influence: Develop skills in influencing product strategy and roadmap decisions at a senior level.

• Cross-Disciplinary Mastery: Gain comprehensive experience integrating UX research, behavioral science, data science, and experimentation.

• Industry Thought Leadership: Contribute to building Safelite's reputation as an innovator in predictive customer experience, potentially through internal presentations or external engagement.

• Mentorship and Skill Development: Opportunities to mentor junior team members and stay current with industry trends through training, conferences, and self-directed learning.

📝 Enhancement Note: This role presents a significant opportunity for growth by tackling complex, foundational challenges within Safelite's digital product organization. The challenges are inherent to building new capabilities, which also translates into substantial learning and development opportunities for the individual.

💡 Interview Preparation

Strategy Questions:

• "How would you approach building a predictive model to anticipate customer churn for Safelite's services, and what data sources would you prioritize?"

• "Describe a time you used a combination of quantitative behavioral data and qualitative research to uncover a significant user insight. What was the outcome?"

• "How would you design an experiment to test the impact of a new feature on customer engagement, considering potential confounding factors?"

• "Imagine you've built a predictive model that shows a 70% confidence level for a certain customer behavior. How would you communicate this to a product manager who needs to make an investment decision?"

• "What are the key ethical considerations when using AI and predictive modeling to understand and influence customer behavior?" Company & Culture Questions:

• "Based on your understanding of Safelite, where do you see the biggest opportunities for applying predictive experience research?"

• "How do you stay current with advancements in UX research, data science, and AI?"

• "Describe your preferred approach to collaborating with product managers and designers. How do you ensure your insights are acted upon?"

• "What does 'learning velocity' mean to you in the context of product development, and how would you measure it?"

• "How do you handle situations where your data-driven recommendations conflict with stakeholder intuition or existing beliefs?" Portfolio Presentation Strategy:

• Focus on Impact: For each case study, clearly articulate the business problem, the specific actions you took, the insights generated, and the measurable impact on key business metrics (e.g., conversion rates, customer satisfaction, efficiency gains, cost savings).

• Explain Your 'Why': Be prepared to justify your methodological choices. Why did you choose a specific model? Why combine quantitative and qualitative data? Why was a particular experiment designed a certain way?

• Demonstrate Predictive Thinking: Highlight projects where you anticipated future behavior, simulated outcomes, or built models that forecast user responses.

• Showcase Communication: Practice explaining complex findings and methodologies in a way that is accessible to non-technical audiences. Use clear language and visuals.

• Address Uncertainty: Be transparent about the limitations of your work, the assumptions made, and how you quantified uncertainty. This demonstrates critical thinking and maturity.

📝 Enhancement Note: Interview preparation for this role should focus on demonstrating a blend of advanced technical skills, strategic thinking, and strong communication abilities. Candidates need to showcase their ability to not only perform sophisticated analysis but also translate those findings into actionable business strategies that drive product innovation and customer satisfaction.

📌 Application Steps

To apply for this UX & Predictive Experience Lead Researcher position:

• Submit your application through the provided Workday portal link: [https://belron.wd3.myworkdayjobs.com/Safelite_Careers/job/OH---STATEWIDE-or-REMOTE/UX---Predictive-Experience-Lead-Researcher_JR74214]

• Customize Your Resume: Tailor your resume to highlight experience in UX research, behavioral science, data science, predictive modeling, AI, experimental design, and proficiency in Python/R/SQL. Quantify your achievements with specific metrics whenever possible.

• Prepare Your Portfolio: Curate 3-4 strong case studies that showcase your ability to build predictive models, integrate diverse data sources, and drive product decisions. Focus on projects with clear impact and measurable outcomes.

• Practice Your Presentation: Rehearse presenting your portfolio projects, focusing on clear communication of the problem, your approach, insights, and impact. Be ready to discuss your methodologies and strategic thinking.

• Research Safelite: Understand Safelite's business, its digital transformation goals, and its customer base. Consider how your skills can directly contribute to their success in the auto glass industry.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.