Lead UX Behavioral Scientist

📍 Job Overview

Job Title: Lead UX Behavioral Scientist

Company: UKG

Location: Lowell, MA, US; Atlanta, GA, US; Sunrise, FL, US

Job Type: FULL_TIME

Category: User Experience Research / Behavioral Science / Data Analytics

Date Posted: 2026-08-28

Experience Level: 10+ years (implied from 7+ years requirement and Lead title)

Remote Status: Hybrid

🚀 Role Summary

• Drive the development of UKG's AI-first product experiences through expert application of behavioral science and data analytics.

• Lead and manage comprehensive user behavior and sentiment analysis programs, leveraging product telemetry and user interaction data.

• Develop predictive and descriptive behavioral insights to enhance customer value realization, optimize AI reliance, and inform product strategy.

• Establish and evolve key behavioral metrics that quantify adoption, trust, and sustained usage of AI-powered features.

📝 Enhancement Note: The role of "Lead UX Behavioral Scientist" implies a senior individual contributor or a foundational leadership role within the UX Research team. The focus on AI-first products, behavioral telemetry, and cross-functional partnerships with Data Science and Product Operations indicates a strong emphasis on quantitative methods and driving measurable outcomes through data. The "10+ years" implied experience level suggests a need for seasoned professionals capable of strategic thinking and mentoring.

📈 Primary Responsibilities

• Conduct rigorous quantitative and behavioral research using product telemetry, log data, and user sentiment to model adoption, reliance (follow/verify/override), intent, and outcome patterns that improve how UKG’s AI products deliver value to customers and end users.

• Develop predictive and descriptive behavioral insights that identify drivers of adoption, friction, hesitation, and abandonment across diverse user segments and roles within the workforce.

• Design and operationalize robust behavioral telemetry and measurement frameworks, instrumenting user interactions, workflows, and touchpoints to generate leading indicators of trust, reliance, and value realization in AI-driven features.

• Partner closely with Product Management, Data Science, and Product Operations teams to build scalable systems for behavioral data collection, experimentation, and continuous insight generation, fostering a data-driven product development lifecycle.

• Lead causal and experimental research initiatives, including A/B tests and quasi-experiments, to rigorously evaluate the impact of new features, nudges, defaults, and varying automation levels on user behavior and critical business outcomes.

• Translate complex behavioral and product data into clear, actionable insights and decision frameworks that directly inform product strategy, focusing on optimizing time-to-value, confidence-to-action, and appropriate AI reliance.

• Establish and evolve key performance indicators (KPIs) and metrics that move beyond traditional sentiment and usability measures to quantitatively assess behavioral outcomes, such as adoption rates, trust calibration, sustained usage patterns, and intervention effectiveness.

• Provide strategic thought leadership on AI adoption and behavioral science principles, actively shaping product direction and influencing senior stakeholders with a clear, evidence-based perspective grounded in user behavior.

• Elevate the quantitative and data science capabilities of the User Research team by mentoring other team members on applying advanced analytics, experimentation, and behavioral frameworks to product development challenges.

• Raise the methodological bar within the broader User Research team and contribute to a culture of continuous improvement in data science and quantitative research practices.

📝 Enhancement Note: The responsibilities highlight a blend of hands-on research execution and strategic influence. The emphasis on "AI-first," "behavioral telemetry," and "predictive/descriptive insights" points towards a role that bridges UX research with data science, requiring a deep understanding of user behavior modeling within complex software systems. The leadership aspect is evident in the responsibility to "elevate team capabilities" and "provide thought leadership."

🎓 Skills & Qualifications

Education: While not explicitly stated, a Master's or Ph.D. in a quantitative field such as Behavioral Science, Psychology (with a quantitative focus), Human-Computer Interaction (HCI), Data Science, Statistics, Economics, or a related discipline is strongly preferred for a Lead Behavioral Scientist role.

Experience: 7+ years of progressive experience in behavioral research, behavioral analytics, data science, or a closely related field, with a proven track record of driving measurable business and user outcomes specifically within digital product environments, particularly AI-first or complex enterprise SaaS platforms.

Required Skills:

• Demonstrated expertise in conducting quantitative and behavioral research at scale, utilizing methods such as experimentation/A/B testing, predictive modeling, segmentation and cluster analysis, conjoint modeling, and detailed behavioral telemetry analysis.

• Strong proficiency with SQL for querying large-scale behavioral and product datasets to extract actionable insights.

• Experience in building research data infrastructure, instrumentation, and reporting frameworks within fast-moving product organizations.

• Ability to translate complex behavioral and product data into actionable recommendations and influence cross-functional stakeholders (Product, Product Operations, Data Science, Engineering, UX).

• Deep understanding of user behavior modeling, including adoption, trust, reliance (follow/verify/override), friction, and outcome patterns.

• Experience with experimental design and causal inference to evaluate feature impact.

• Excellent communication skills, with the ability to present complex data and insights clearly to technical and non-technical audiences, including executive stakeholders.

• Strong analytical, reasoning, and problem-solving skills with a curious and entrepreneurial mindset. Preferred Skills:

• Experience delivering AI-assisted and conversational experiences, including familiarity with Machine Learning (ML) or Natural Language Processing (NLP), conversational analytics, and journey or intent modeling.

• Proficiency with data visualization and business intelligence tools such as Power BI (preferred), QuickSight, Looker, or Tableau.

• Familiarity with AI-assisted analytics platforms like Claude.

• Experience within enterprise SaaS, workforce technology, HR technology, or AI-enabled productivity platforms.

• Experience in mentoring junior researchers or analysts in quantitative methods and data science.

📝 Enhancement Note: The "7+ years" requirement combined with the "Lead" title strongly suggests a senior-level role, often associated with 10+ years of cumulative experience. The emphasis on specific quantitative methods (predictive modeling, segmentation, conjoint analysis) and data infrastructure points to a need for a scientist who can not only analyze but also help build the systems for data collection and analysis. The preference for Power BI and familiarity with Claude highlight specific toolchain expectations.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrate a portfolio showcasing successful projects where quantitative behavioral research and data analysis directly influenced product strategy and led to measurable improvements in user adoption, AI reliance, or key business outcomes.

• Include case studies detailing the design and execution of experiments (e.g., A/B tests) and the resulting impact on user behavior and product performance.

• Provide examples of developed behavioral telemetry frameworks, instrumentation strategies, and the resulting data insights generated.

• Showcase experience in building dashboards or reporting frameworks to visualize key behavioral metrics and communicate insights to stakeholders. Process Documentation:

• Document methodologies for designing and implementing behavioral data collection strategies, ensuring data integrity and relevance to product hypotheses.

• Illustrate processes for translating raw behavioral data into actionable insights and clear recommendations for product teams.

• Present frameworks for continuous insight generation and the integration of behavioral data into the product development lifecycle, including experimentation and optimization phases.

📝 Enhancement Note: For a Lead Behavioral Scientist role, a portfolio is crucial. It should highlight not just the ability to conduct research but also to operationalize it, build data infrastructure, and translate findings into strategic product decisions. Case studies should emphasize the "lead" aspect, showing initiative, problem-solving, and measurable impact.

💵 Compensation & Benefits

Salary Range: $145,600 - $209,300 annually.

• This range is competitive for a Lead Behavioral Scientist role in major US tech hubs like Lowell, MA, Atlanta, GA, and Sunrise, FL, reflecting the required senior-level experience and specialized skillset. The actual base pay will depend on factors such as specific location (cost of living differences), depth of experience, and negotiation. Benefits:

• Performance-based bonus plan: Opportunity to earn additional compensation based on individual and company performance.

• Restricted stock unit (RSU) awards: Potential for equity ownership in UKG, aligning employee success with company growth.

• Comprehensive health, dental, and vision insurance.

• Retirement savings plan (e.g., 401k) with company match.

• Generous paid time off (PTO), holidays, and potential for floating holidays.

• Flexible work arrangements (Hybrid model implies flexibility in work location and schedule).

• Professional development opportunities, including training, conferences, and access to learning resources.

• Employee Assistance Program (EAP) for well-being support.

• Potential for other perks such as wellness programs, commuter benefits, and employee discounts.

Working Hours: 40 hours per week (standard full-time). The hybrid work arrangement suggests flexibility in structuring these hours, with an expectation of collaboration during core business hours.

📝 Enhancement Note: The provided salary range is within the expected band for a Lead-level role in these locations. The mention of performance-based bonuses and RSUs indicates a total compensation package that goes beyond base salary, common for senior positions in publicly traded or significant private tech companies. The "Flexible work arrangements" is a key benefit for hybrid roles.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology, specifically Workforce Management and HR Technology (SaaS). UKG is a major player in this space, offering a comprehensive "Workforce Operating Platform" powered by AI.

Company Size: Large enterprise (implied by the scale of operations and product offerings; UKG is known to be a significant employer with thousands of employees globally). This size suggests established processes, resources, and opportunities for broad impact.

Founded: UKG was formed in 2020 through the merger of Ultimate Software and Kronos Incorporated, bringing together deep expertise in HR and workforce management technology. This history suggests a culture that values innovation, customer focus, and employee well-being.

Team Structure:

• The Lead UX Behavioral Scientist will be a key member of the UX Research team, reporting to the Head of UX Research.

• This role involves close collaboration with Product Management, Data Science, Product Operations, and Engineering teams, indicating a highly cross-functional environment.

• The team structure likely supports specialization, with this role focusing on quantitative behavioral insights to complement qualitative UX research efforts. Methodology:

• UKG emphasizes an "AI-first" approach, integrating artificial intelligence across its product suite to drive understanding and empower users.

• The company values a "people-first" culture, focusing on employee well-being, collaboration, and customer success.

• Data-driven decision-making is core, with a strong reliance on insights from product telemetry, user analytics, and behavioral science.

• The company champions continuous learning, innovation, and a collaborative, inclusive environment.

Company Website: ukg.com

📝 Enhancement Note: The company's focus on "AI-first" and a "people-first" culture, combined with its large enterprise status and merger history, suggests a dynamic environment that balances established practices with forward-looking innovation. The role's success will depend on navigating this complex organizational landscape and effectively collaborating across diverse teams.

📈 Career & Growth Analysis

Operations Career Level: Lead Behavioral Scientist. This signifies a senior individual contributor role with significant autonomy and influence. It involves not only executing complex research but also shaping research strategy, mentoring others, and acting as a subject matter expert in behavioral science and data analytics within the UX organization.

Reporting Structure: Reports to the Head of UX Research, placing this role within the core UX function but with strong ties to Product, Data Science, and Product Operations. This structure allows for direct impact on product strategy and development.

Operations Impact: The role has a direct impact on the success of UKG's AI-first products by providing critical insights into user behavior, trust, and value realization. This influence shapes product roadmaps, feature development, and overall user experience, ultimately driving customer adoption, retention, and satisfaction, which are key revenue drivers.

Growth Opportunities:

• Leadership Advancement: Potential to move into a management role within UX Research, leading a team of behavioral scientists or quantitative researchers, or to become a Principal/Distinguished Scientist focusing on specialized areas of AI or behavioral analytics.

• Skill Specialization: Deepen expertise in specific areas of AI, machine learning, advanced statistical modeling, or causal inference through challenging projects and company-sponsored learning opportunities.

• Cross-Functional Leadership: Grow into influencing product strategy at a higher level, potentially leading initiatives that span multiple product lines or departments, leveraging behavioral insights to drive broader organizational change.

• Industry Recognition: Contribute to the company's thought leadership in AI and behavioral science, potentially through publications, conference presentations, or internal knowledge-sharing initiatives.

📝 Enhancement Note: A "Lead" title at this level typically indicates a path towards Principal or management roles. The emphasis on AI and data science within a large SaaS company offers significant opportunities for growth in specialized, high-demand fields. The reporting structure to the Head of UX Research suggests a clear career trajectory within the UX discipline.

🌐 Work Environment

Office Type: Hybrid. This means employees are expected to work from a UKG office location some days of the week, with the flexibility to work remotely on other days. This offers a balance between in-person collaboration and individual focused work.

Office Location(s): Lowell, MA; Atlanta, GA; Sunrise, FL. These are established corporate office locations for UKG, likely equipped with modern amenities to support hybrid work, including collaborative spaces, quiet zones, and necessary technology infrastructure.

Workspace Context:

• Collaborative Environment: The hybrid model necessitates intentional collaboration. Expect opportunities to work closely with UX designers, researchers, product managers, and data scientists in shared spaces or through effective virtual collaboration tools.

• Tools & Technology: Access to robust internal IT infrastructure, including powerful workstations, reliable network access, and the necessary software licenses for analytics, visualization, and communication tools.

• Team Interaction: Regular team meetings, brainstorming sessions, and cross-functional project syncs, both in-person and virtually, to foster a connected and productive work environment.

Work Schedule: Standard 40-hour work week, with flexibility in daily scheduling due to the hybrid arrangement. Core hours will likely be established for essential team collaboration, but there's typically autonomy in managing individual work time.

📝 Enhancement Note: The hybrid nature of this role implies a need for strong self-management, excellent communication skills, and the ability to be productive both independently and collaboratively. The specific office locations suggest these are key hubs for the company's operations and R&D.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter or hiring manager will review your application and resume, focusing on alignment with the required experience and skills. Be prepared to articulate your quantitative research background and experience with AI products.

• Technical/Behavioral Interview: Expect interviews with members of the UX Research team and potentially Data Science or Product Management. These will delve into your experience with behavioral research methodologies, SQL proficiency, A/B testing, predictive modeling, and your approach to extracting insights from complex data. Case studies or hypothetical problem-solving scenarios related to AI product adoption and user trust are likely.

• Portfolio Review: A dedicated session to present your portfolio. This is critical for demonstrating your practical application of skills. Prepare 2-3 in-depth case studies that highlight your contributions to product strategy through behavioral science and data analysis.

• Cross-Functional Interviews: Meetings with stakeholders from Product, Product Operations, or Engineering to assess your collaboration style, ability to influence, and understanding of product development processes.

• Final Interview: Likely with the hiring manager or a senior leader to assess overall fit, leadership potential, and strategic alignment.

Portfolio Review Tips:

• Structure: For each case study, clearly define the problem, your role and methodology, the data used, your key insights, the actions taken based on your insights, and the measurable outcomes achieved.

• Quantify Impact: Whenever possible, use metrics and data to demonstrate the tangible impact of your work on user behavior, product adoption, or business objectives.

• Highlight AI/Behavioral Focus: Emphasize projects involving AI products, user trust, reliance, adoption challenges, or complex behavioral modeling. Showcase your ability to translate behavioral data into actionable product decisions.

• Tool Proficiency: Be ready to discuss your experience with SQL, Power BI, and other relevant analytics tools, perhaps even demonstrating a dashboard or query if appropriate.

• Conciseness: Present your work clearly and concisely, focusing on the most impactful aspects. Be prepared for follow-up questions that probe deeper into your process and decision-making.

Challenge Preparation:

• Be ready for a take-home assignment or a live coding/analysis challenge involving a dataset or a product scenario. This will likely test your SQL skills, analytical thinking, and ability to derive insights.

• Practice articulating your thought process clearly and logically, especially when facing ambiguity or incomplete data.

• Prepare to discuss how you would approach measuring the success of AI features or identify friction points in a user journey using behavioral data.

📝 Enhancement Note: The interview process for this role is heavily focused on quantitative skills and the ability to demonstrate impact through a portfolio. Candidates should be prepared to "show, don't just tell" their expertise in behavioral science applied to product development.

🛠 Tools & Technology Stack

Primary Tools:

• SQL: Essential for data extraction and manipulation from large-scale behavioral and product datasets.

• Power BI (preferred): For creating dashboards, visualizations, and executive-ready narratives.

• QuickSight, Looker, Tableau: Alternative or complementary data visualization and business intelligence tools.

• Claude (and other AI-assisted analytics platforms): For leveraging AI to enhance data analysis, insight generation, and potentially conversational analytics.

Analytics & Reporting:

• Product Telemetry/Instrumentation Tools: Tools used to capture user interactions, events, and workflows within the UKG platform (specifics may vary internally).

• A/B Testing Platforms: Tools for designing, running, and analyzing experiments to evaluate feature impact.

• Data Warehousing/Lake: Experience working with data stored in large-scale data platforms.

CRM & Automation:

• While not a primary focus for this role, understanding how behavioral data integrates with CRM and automation systems to influence customer journeys and personalized experiences is beneficial.

• Familiarity with enterprise SaaS platforms and HR/Workforce Management systems is a plus.

📝 Enhancement Note: The explicit mention of SQL and specific BI tools (Power BI, QuickSight, Looker, Tableau) plus AI platforms like Claude, indicates the core technical stack. Proficiency here is non-negotiable. The role requires someone who can work with raw data and transform it into actionable intelligence using these tools.

👥 Team Culture & Values

Operations Values:

• People-First: UKG emphasizes a supportive and inclusive culture where employees are valued and respected. This translates to empathetic research and a focus on user well-being.

• Data-Driven: Decisions are grounded in evidence. A commitment to rigorous data analysis, experimentation, and behavioral insights is paramount.

• Innovation & AI-First: A forward-thinking mindset, embracing new technologies like AI to solve complex problems and create cutting-edge products.

• Collaboration & Entrepreneurial Spirit: Working effectively across teams, sharing knowledge, and taking initiative to drive improvements and solve challenges.

• Curiosity & Problem-Solving: A natural inclination to ask "why," explore complex issues, and develop creative solutions.

Collaboration Style:

• Cross-Functional Integration: The role requires seamless collaboration with Product Managers, Data Scientists, UX Designers, and Product Operations. Expect active participation in product strategy sessions, feature design reviews, and data review meetings.

• Evidence-Based Communication: Presenting findings and recommendations with clear, data-backed arguments to influence stakeholders and gain buy-in.

• Knowledge Sharing: A culture that encourages sharing best practices, learnings from experiments, and insights across teams to foster collective growth and improve overall product quality.

📝 Enhancement Note: The "people-first" and "AI-first" values are central to UKG's identity. Candidates should demonstrate how their work aligns with these principles, showcasing empathy in user research and a proactive approach to leveraging AI for product enhancement.

⚡ Challenges & Growth Opportunities

Challenges:

• Complexity of AI Adoption: Understanding and influencing user trust, reliance, and adoption of complex AI features, especially in enterprise environments where change can be slow.

• Data Integration & Infrastructure: Ensuring robust and scalable data collection and analysis systems are in place to support ongoing behavioral insights generation.

• Translating Insights to Action: Effectively communicating complex behavioral findings to diverse stakeholders and driving product teams to implement recommended changes.

• Measuring Behavioral Impact: Developing and refining metrics that accurately capture the nuanced behavioral outcomes of AI-driven features beyond basic usage.

• Keeping Pace with AI: Continuously learning and adapting to the rapidly evolving landscape of AI and its implications for user behavior and product design.

Learning & Development Opportunities:

• Advanced Analytics & ML: Opportunities to deepen expertise in machine learning, predictive modeling, and causal inference techniques through challenging projects and internal/external training.

• AI & Conversational Design: Exposure to cutting-edge AI applications and conversational interfaces, fostering development in related analytical areas.

• Industry Conferences & Certifications: Support for attending relevant conferences (e.g., UX, Data Science, AI) and pursuing certifications.

• Mentorship & Leadership: Guidance from senior leaders and opportunities to mentor junior team members, fostering leadership development.

• Cross-Functional Exposure: Gaining deeper understanding of product management, engineering, and business operations through close collaboration.

📝 Enhancement Note: The challenges are directly tied to the cutting-edge nature of AI product development and the complexities of enterprise SaaS. The growth opportunities are significant, offering a path to becoming a leading expert in a high-demand field.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you used behavioral data to significantly influence product strategy or improve a key user metric. What was your process, and what were the measurable outcomes?" (Focus on problem definition, data analysis, insight generation, action, and impact.)

• "How would you approach measuring user trust and appropriate reliance on an AI-powered feature within our workforce platform? What behavioral indicators would you track?" (Assess your understanding of AI-specific behavioral metrics.)

• "Walk us through an A/B test you designed and executed. What was the hypothesis, how did you set it up, what were the results, and what did you learn?" (Demonstrate experimental design and analysis skills.) Company & Culture Questions:

• "What do you know about UKG's AI strategy and our workforce management products? How does your expertise in behavioral science align with our 'AI-first' and 'people-first' values?" (Show your research and understanding of the company's mission.)

• "How do you foster collaboration with product managers and engineers who may have different perspectives on data or user behavior?" (Assess your cross-functional communication and influence skills.)

• "Describe a situation where you had to deliver difficult news or insights based on your data analysis. How did you communicate it, and what was the outcome?" (Evaluate your ability to handle constructive conflict and deliver impactful feedback.) Portfolio Presentation Strategy:

• Storytelling: Frame your case studies as compelling narratives. Clearly articulate the "why" behind each project and the "so what" of your findings.

• Visual Aids: Use clear, professional slides with key data points, visualizations, and process flows. Avoid overwhelming slides with text.

• Focus on Impact: Quantify your contributions and the resulting business or user impact. Use metrics to demonstrate success.

• Methodological Rigor: Be prepared to discuss the nuances of your chosen methodologies, including any limitations or assumptions.

• Interactive Q&A: Anticipate detailed questions about your process, data sources, statistical methods, and decision-making. Be ready to defend your approaches and discuss alternative solutions.

📝 Enhancement Note: Interview preparation should focus on demonstrating a strong understanding of behavioral science principles, quantitative analysis, and their application to AI products. Candidates should be ready to articulate their impact using data and storytelling through their portfolio.

📌 Application Steps

To apply for this Lead UX Behavioral Scientist position:

• Submit your application through the UKG careers portal via the provided link.

• Tailor your resume: Highlight your 7+ years of experience in behavioral research, data science, and quantitative analysis. Emphasize specific achievements related to AI products, product telemetry, A/B testing, and deriving actionable insights from complex datasets.

• Curate your portfolio: Select 2-3 of your strongest projects that showcase your ability to conduct behavioral research, build data infrastructure, analyze product telemetry, and influence product strategy with data-driven insights. Ensure each project clearly outlines the problem, your methodology, key findings, actions taken, and measurable outcomes.

• Prepare for technical assessments: Brush up on your SQL skills and practice problem-solving scenarios involving data analysis and experimental design. Be ready to discuss your experience with data visualization tools like Power BI.

• Research UKG: Understand their AI-first strategy, their workforce management platform, and their stated company values ("people-first," innovation, collaboration). Prepare to discuss how your background and approach align with these.

⚠️ 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.

---