Quantitative UX Researcher, Corporate Engineering

📍 Job Overview

Job Title: Quantitative UX Researcher, Corporate Engineering

Company: Google

Location: Hyderabad, Telangana, India

Job Type: Full-Time

Category: Data Science / Research Operations

Date Posted: May 28, 2026

Experience Level: 2-5 Years

Remote Status: On-site

🚀 Role Summary

• Drive the definition and measurement of quantitative UX goals and metrics for AI-powered workflows and physical security platforms within Corporate Engineering.

• Apply advanced statistical and data science techniques, including experimentation, to analyze user behavior and inform product strategy.

• Collaborate closely with Product Management and Engineering teams to translate complex statistical findings into actionable product requirements and data pipeline definitions.

• Develop and execute research roadmaps, leveraging empirical methods like log analysis, survey research, and regression to uncover actionable insights for product innovation.

• Communicate data-backed narratives and technical analysis to diverse stakeholders, ensuring alignment on user needs and product strategy for scalable business solutions.

📝 Enhancement Note: This role is situated within Google's "Corporate Engineering" division, focusing on internal business solutions and infrastructure ("Google for Googlers"). The emphasis on "AI-driven innovation across workflows" and "physical security and workplace experience platforms" highlights a unique application of UX research in an enterprise IT context, requiring a blend of user-centric methodologies and robust data science capabilities. The role is inherently operations-focused, aiming to optimize internal systems and user experiences that underpin Google's global operations.

📈 Primary Responsibilities

• Define, instrument, and measure quantitative UX goals and metrics for AI-driven workflows and workplace experience platforms, driving adoption of necessary data infrastructure.

• Execute advanced statistical analyses using diverse data sources, including system logs and survey data, to identify key drivers of AI success and understand complex user behaviors.

• Develop and implement a project-level research roadmap, employing experimental design and data science techniques to inform product strategy and innovation.

• Collaborate cross-functionally with Product Management and Engineering to identify high-impact research questions, translate statistical findings into clear product requirements, and define data pipeline needs.

• Create compelling, data-backed narratives and visualizations that communicate user behaviors and technical analysis to stakeholders, ensuring alignment and driving data-informed decision-making.

• Design and implement experimentation frameworks to iterate on AI-powered workflows, leveraging live sandboxes for continuous improvement and hypothesis testing.

• Analyze multimodal data inputs, integrating various data streams to provide comprehensive insights into user experience and product interaction.

• Influence product and engineering directions by leading multiple projects focused on improving quantitative insights, including instrumentation, measurement definition, metrics tracking, and statistical analysis.

📝 Enhancement Note: The responsibilities emphasize a strong analytical and strategic component, requiring the candidate to not only analyze data but also to proactively shape research direction and influence product roadmaps. The focus on "AI-driven innovation" and "physical security and workplace experience platforms" indicates a need for specialized domain understanding within enterprise IT and advanced analytics.

🎓 Skills & Qualifications

Education:

• Bachelor's degree in Human-Computer Interaction, Computer Science, Statistics, Psychology, Anthropology, Data Science, or a related quantitative field, or equivalent practical experience.

Experience:

• Minimum of 4 years of experience in product research within an applied research setting, or a similar role focused on quantitative analysis and user experience.

• Experience in programming languages commonly used for data manipulation and computational statistics, such as Python, R, MATLAB, C++, Java, or Go.

• Demonstrated experience in programming computational and statistical algorithms for analyzing large datasets.

• Proficiency in using SQL, R, or Python for analyzing large datasets and user logs to complement primary research findings.

• Experience designing advanced evaluation frameworks and processing multimodal data inputs.

• Understanding of user research questions and the technical tools required to analyze data and measure product user experience and interaction.

Required Skills:

• Quantitative UX Research: Proven ability to design and execute quantitative research studies, analyze data, and derive actionable insights.

• Statistical Analysis & Programming: Expertise in statistical techniques and proficiency in programming languages like Python or R for data manipulation, analysis, and modeling.

• Data Analysis & Interpretation: Ability to analyze large datasets, user logs, and survey data to identify patterns, trends, and key drivers of user behavior and product success.

• Experimental Design: Experience in designing and conducting experiments, including A/B testing and hypothesis-driven research, to evaluate product changes and AI feature effectiveness.

• Human-Computer Interaction (HCI): Strong understanding of HCI principles to inform research design and product evaluation.

Preferred Skills:

• Large-Scale Data Analysis: Experience with SQL and handling of very large datasets, including user logs and operational data.

• Computational Statistics: Proficiency in developing and implementing computational and statistical algorithms.

• Multimodal Data Processing: Experience in integrating and analyzing data from various sources (e.g., system logs, surveys, user feedback).

• Metrics Definition & Tracking: Ability to define, instrument, and track key performance indicators (KPIs) and user experience metrics.

• Product Strategy Influence: Demonstrated ability to translate research findings into concrete product requirements and influence product roadmaps.

📝 Enhancement Note: The requirements clearly indicate a need for a hybrid profile, combining deep expertise in quantitative research methodologies with strong data science and programming skills. The emphasis on applied research and influencing product direction suggests a role that sits at the intersection of research, product, and engineering.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Case Studies of Quantitative Research Projects: Showcase at least 2-3 detailed case studies demonstrating your approach to defining research questions, designing methodologies, collecting and analyzing quantitative data, and delivering actionable insights.

• Data Analysis & Statistical Modeling Examples: Include examples of your work with large datasets, statistical modeling, regression analysis, or experimental design, highlighting the tools and techniques used (e.g., Python scripts, R code snippets, SQL queries).

• Metrics Definition & Impact Measurement: Provide examples where you defined key UX metrics, instrumented systems for measurement, and tracked performance over time, demonstrating the impact of your research on product improvements or business outcomes.

• Cross-Functional Collaboration Examples: Illustrate instances where your research directly informed product or engineering decisions, showcasing your ability to translate complex findings into clear product requirements and communicate effectively with diverse stakeholders.

Process Documentation:

• Research Design & Execution: Document your systematic approach to designing quantitative research studies, including hypothesis formulation, methodology selection, and sampling strategies.

• Data Analysis & Interpretation Frameworks: Outline your standard processes for data cleaning, statistical analysis, and interpretation, emphasizing rigor and validity.

• Instrumentation & Measurement Strategy: Demonstrate your understanding of how to work with engineering teams to define and implement data instrumentation for effective user behavior tracking and metric measurement.

• Reporting & Communication Protocols: Showcase your methods for creating clear, data-backed narratives and presenting findings to technical and non-technical audiences, ensuring actionable recommendations are understood.

📝 Enhancement Note: For a Quantitative UX Researcher role, the portfolio is critical. It should not just list skills but demonstrate practical application. Candidates should be prepared to walk through their case studies, explaining their thought process, challenges faced, and the quantitative impact of their work. The focus on "Corporate Engineering" suggests a need to show how research drives efficiency and scalability within internal systems.

💵 Compensation & Benefits

Salary Range:

Benefits:

• Comprehensive Health Insurance: Medical, dental, and vision coverage for employees and dependents.

• Retirement Savings Plans: Generous contributions to employee provident funds and other retirement schemes.

• Paid Time Off: Ample vacation days, sick leave, and public holidays, with potential for additional paid time off for professional development.

• Professional Development: Opportunities for training, conferences, workshops, and access to internal learning resources to enhance skills in data science, statistics, and UX research.

• Wellness Programs: Access to fitness centers, mental health support services, and employee assistance programs.

• Parental Leave: Generous paid leave for new parents.

• On-site Amenities: Depending on the Hyderabad campus, this may include subsidized meals, on-site health services, and recreational facilities.

• Stock Options/RSUs: Potential for equity compensation as part of the overall compensation package.

Working Hours:

• Standard full-time workweek, typically 40 hours per week. While the role is on-site, Google generally offers a degree of flexibility in daily working hours to accommodate personal needs, provided core collaboration hours are met and project deadlines are achieved.

📝 Enhancement Note: Google is known for its comprehensive and competitive benefits packages. The salary range is an estimate based on publicly available data for similar roles in Hyderabad and Google's typical compensation structure. Actual compensation will be determined by individual experience, qualifications, and negotiation.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology (Internet Services & Software)

Company Size: Large Enterprise (10,000+ employees)

Founded: 1998

Company Slogan: "Organize the world's information and make it universally accessible and useful."

Company Description: Google is a global technology leader focused on AI, search, cloud computing, hardware, and advertising. Its mission is to build products and services that improve the lives of billions worldwide. Corporate Engineering (Corp Eng) specifically focuses on building and maintaining the internal systems and infrastructure that enable Google's operations and employee productivity.

Team Structure:

• Quant UXR within Corporate Engineering: This team likely comprises a specialized group of Quantitative UX Researchers embedded within specific engineering divisions (e.g., Spaces UX).

• Cross-Functional Collaboration: Researchers work closely with Product Managers, Software Engineers, Data Scientists, and Designers to define, develop, and launch internal tools and platforms.

• Reporting: Researchers typically report into a UXR leadership structure or a dedicated Research Operations/Product Insights group within Corp Eng, with strong dotted-line reporting to product and engineering leads for project alignment.

Methodology:

• Data-Driven Decision Making: Emphasis on empirical evidence, quantitative analysis, and experimentation to guide product development and optimization.

• User-Centric Innovation: Focus on understanding and serving the needs of internal Google users (Googlers) to enhance productivity and experience.

• Scalable Solutions: Development of robust, reliable, and scalable systems that support Google's global operations.

• Continuous Improvement: Iterative development cycles driven by ongoing research, data analysis, and performance monitoring.

Company Website: https://www.google.com

📝 Enhancement Note: The "Corporate Engineering" context is crucial. Unlike product UX roles for external users, this role focuses on optimizing the internal experience for Google employees. This requires understanding enterprise workflows, productivity tools, and the unique challenges of supporting a massive, complex organization. The "Google for Googlers" ethos implies a deep commitment to internal user satisfaction.

📈 Career & Growth Analysis

Operations Career Level: This role aligns with a mid-level to senior Quantitative UX Researcher position. It requires independent project management, advanced analytical skills, and the ability to influence product strategy. The scope involves driving impact across multiple projects related to AI-driven workflows and workplace experience platforms.

Reporting Structure: The role reports into a UXR leadership or Research Operations function within Corporate Engineering. Direct collaboration and influence will be exerted on Product Managers and Engineering teams responsible for the physical security and workplace experience platforms.

Operations Impact: The Quantitative UX Researcher will directly impact the efficiency, productivity, and satisfaction of Google employees by improving internal tools, AI-driven workflows, and workplace experience platforms. This role is critical for optimizing operational effectiveness and supporting the development of new technologies within Google.

Growth Opportunities:

• Specialization: Deepen expertise in AI-driven workflows, workplace experience research, or advanced statistical modeling within an enterprise context.

• Leadership: Progress into senior researcher roles, team lead positions, or management roles within the UXR or Research Operations function.

• Cross-Functional Movement: Opportunities to move into Product Management, Data Science, or Research Operations roles within Google, leveraging a strong understanding of user behavior and data analysis.

• Skill Development: Continuous learning through internal Google resources, mentorship programs, and exposure to cutting-edge research challenges and technologies.

• Mentorship: Access to a supportive Quant UXR community offering mentorship and professional development opportunities.

📝 Enhancement Note: The growth path here is strong, leveraging Google's internal development culture. The role offers a unique opportunity to apply advanced research techniques to critical internal operations, paving the way for specialized leadership or broader product/data roles within the company.

🌐 Work Environment

Office Type: While the role is on-site, Google's offices are known for fostering a collaborative and innovative environment, often featuring open-plan areas, dedicated project rooms, and comfortable informal meeting spaces.

Office Location(s): Hyderabad, Telangana, India. Specific campus details would be provided during the application process.

Workspace Context:

• Collaborative Spaces: Access to modern office facilities designed to encourage teamwork, brainstorming, and cross-functional interaction.

• Tools & Technology: Equipped with high-performance computing resources, access to Google's internal software and data platforms, and advanced analytics tools.

• Team Interaction: Frequent opportunities to engage with a diverse group of engineers, product managers, and fellow researchers, fostering a dynamic learning environment.

Work Schedule: The role is on-site, requiring presence at the Hyderabad office. While a standard 40-hour workweek is expected, Google often promotes flexibility in daily schedules to support work-life balance, provided core collaboration requirements are met and project milestones are achieved. This flexibility can be beneficial for deep analytical work and data processing tasks.

📝 Enhancement Note: The on-site requirement emphasizes the importance of in-person collaboration and leveraging the resources available at Google's Hyderabad campus. The environment is designed to support deep work while encouraging interaction and knowledge sharing.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screen: A recruiter or hiring manager will conduct an initial phone screen to assess basic qualifications, experience, and alignment with the role.

• Technical Phone/Video Interview: One or more interviews focused on quantitative research methodologies, statistical concepts, programming skills (Python/R/SQL), and experimental design.

Expect coding challenges or data interpretation exercises.

• On-site/Virtual Loop: A series of interviews (typically 4-6) with various team members, including researchers, product managers, and engineers. These interviews will cover:

  • Portfolio Review: A dedicated session to present and discuss your research portfolio, focusing on specific case studies.
  • Behavioral Questions: Assessing your collaboration style, problem-solving approach, leadership potential, and fit with Google's culture.
  • Problem-Solving/Case Study: You may be given a hypothetical problem related to AI workflows or workplace experience to analyze and propose research solutions for.
  • Technical Deep Dive: Further exploration of your quantitative skills, statistical knowledge, and experience with relevant tools.

• Hiring Committee Review: Your interview feedback is compiled and reviewed by a hiring committee for a final decision.

Portfolio Review Tips:

• Quantify Impact: For each case study, clearly articulate the problem, your methodology, the key findings, and, most importantly, the impact of your research (e.g., metrics improved, product changes implemented, cost savings).

• Showcase Technical Skills: Be prepared to discuss your code (Python, R, SQL) and statistical models used. Highlight your ability to handle large datasets and complex analytical problems.

• Focus on Relevance: Tailor your portfolio to highlight experience with AI, enterprise systems, productivity tools, or similar areas relevant to Corporate Engineering.

• Tell a Story: Structure your case studies with a clear narrative: the challenge, your approach, the execution, the results, and the learnings.

• Visual Aids: Use clear visualizations (charts, graphs) to present data and findings effectively.

• Prepare for Questions: Anticipate questions about your methodology choices, statistical assumptions, challenges faced, and how you would approach similar problems in the future.

Challenge Preparation:

• Review Core Concepts: Revisit fundamental statistics, experimental design principles, and common UX research methods.

• Practice Coding: Brush up on Python/R for data manipulation, statistical analysis, and SQL for data querying.

• Think Through Problems: Practice approaching hypothetical product or user experience challenges from a quantitative perspective. Consider how you would define metrics, design experiments, and analyze results.

• Understand Google's Products: Familiarize yourself with Google's internal tools and the general landscape of enterprise technology and AI applications.

📝 Enhancement Note: Google's interview process is rigorous. A strong portfolio demonstrating quantitative rigor, impact, and clear communication is essential. Candidates should be prepared to articulate their thought process and justify their methodological choices extensively.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: Python (with libraries like Pandas, NumPy, SciPy, Scikit-learn), R (with extensive statistical packages), MATLAB.

• Data Querying: SQL for accessing and manipulating data from various databases.

• Statistical Software: Experience with statistical packages within R or Python, potentially specialized software if applicable.

Analytics & Reporting:

• Data Visualization: Tools like Matplotlib, Seaborn (Python), ggplot2 (R), or internal Google visualization tools for creating dashboards and reports.

• Log Analysis Tools: Experience with tools for processing and analyzing large-scale system logs.

• Survey Platforms: Tools for designing, distributing, and analyzing survey data.

CRM & Automation:

• Internal Google Platforms: Proficiency with Google's internal data infrastructure, analysis platforms, and project management tools will be developed on the job.

• Experimentation Platforms: Experience with platforms that facilitate A/B testing and experimentation on live products.

• Data Warehousing: Familiarity with concepts and tools related to data warehousing and ETL processes.

📝 Enhancement Note: A strong foundation in Python and R, coupled with SQL, is critical. Candidates should highlight any experience with large-scale data processing, advanced statistical modeling, and data visualization. Familiarity with internal Google systems is not expected upfront but adaptability to learn them quickly is key.

👥 Team Culture & Values

Operations Values:

• Focus on the User: A fundamental principle at Google, extending to internal users. Decisions are driven by understanding and improving the user experience.

• Data-Driven: A strong emphasis on empirical evidence, metrics, and quantitative analysis to inform strategy and measure success.

• Innovation: Encouraging creative problem-solving and the development of novel solutions, particularly with AI.

• Collaboration: Working effectively across diverse teams (Engineering, Product, Design) to achieve shared goals.

• Impact & Scale: Building solutions that have a significant, measurable impact across Google's vast operations.

• Iteration & Learning: Embracing continuous improvement through feedback loops, experimentation, and learning from both successes and failures.

Collaboration Style:

• Cross-Functional Integration: Researchers are expected to be active participants in product teams, working closely with engineers and product managers throughout the development lifecycle.

• Open Communication: A culture that encourages open feedback, constructive criticism, and the sharing of ideas.

• Knowledge Sharing: Regular opportunities for researchers to share learnings, best practices, and insights through internal forums, meetups, and presentations.

• Evidence-Based Debate: Discussions and decision-making are grounded in data and research findings, fostering a rigorous and objective environment.

📝 Enhancement Note: Google's culture values intellectual curiosity, a proactive approach, and a commitment to data integrity. For this role, it means being comfortable diving deep into complex data, challenging assumptions with evidence, and working collaboratively to solve problems that impact millions of users (internal Googlers).

⚡ Challenges & Growth Opportunities

Challenges:

• Complexity of Internal Systems: Navigating and understanding the intricate architecture of Google's internal enterprise systems and platforms.

• Data Availability & Instrumentation: Ensuring adequate data is collected and instrumented to support robust quantitative analysis for new features or evolving workflows.

• Balancing Research Rigor with Speed: Delivering timely insights in a fast-paced product development environment.

• Measuring AI Impact: Quantifying the user experience and operational impact of complex AI-driven features and workflows.

• Cross-Functional Alignment: Gaining buy-in and ensuring consistent understanding of research findings and recommendations across different teams and stakeholders.

Learning & Development Opportunities:

• Advanced Data Science & AI: Opportunities to learn and apply cutting-edge statistical techniques and machine learning models to UX research problems.

• Industry Conferences & Certifications: Support for attending leading UX research and data science conferences, and pursuing relevant certifications.

• Mentorship: Access to experienced researchers and data scientists within Google for guidance and career development.

• Internal Training: Extensive internal learning platforms and workshops covering a wide range of technical and soft skills.

• Exposure to Scale: Working on problems that affect a massive user base within Google, providing unparalleled learning opportunities in managing complexity and scale.

📝 Enhancement Note: The challenges are inherent to working in a large, innovative tech company like Google, particularly within an engineering division. The growth opportunities are substantial, offering a clear path for skill enhancement and career advancement within the data science and research domain.

💡 Interview Preparation

Strategy Questions:

• "Tell me about a time you used quantitative data to significantly influence product strategy or a major design decision. What was the impact?"

  • Preparation: Prepare a detailed case study from your portfolio. Focus on the problem, your quantitative approach, the specific metrics you tracked, the insights you uncovered, how you communicated them, and the resulting product changes/business impact. Quantify the impact as much as possible.

• "How would you approach measuring the user experience of a new AI-powered workflow for internal task management? What metrics would you define, and what data sources would you use?"

  • Preparation: Think about defining key performance indicators (KPIs) for efficiency, task completion, error rates, user satisfaction, and adoption. Consider log data, user surveys, and potentially experimental designs. Structure your answer around a systematic process.

• "Describe a complex statistical problem you encountered in a research project and how you solved it. What were the trade-offs?"

  • Preparation: Be ready to discuss your statistical reasoning, choice of models, assumptions made, and how you validated your results. Highlight your ability to handle ambiguity and technical challenges.

Company & Culture Questions:

• "Why Google Corporate Engineering? What interests you about working on internal tools and infrastructure?"

  • Preparation: Research Corp Eng's mission ("Google for Googlers"), the specific focus areas (AI workflows, workplace experience), and articulate how your skills can drive impact in this unique operational context.

• "How do you collaborate with engineers and product managers? Describe a situation where you had to manage differing opinions or priorities."

  • Preparation: Emphasize your ability to build relationships, communicate technical concepts clearly, listen to feedback, and find common ground based on data and user needs.

• "How do you ensure the quality and validity of your quantitative research findings?"

  • Preparation: Discuss your approach to experimental design, statistical rigor, data cleaning, addressing bias, and triangulating findings from multiple data sources.

Portfolio Presentation Strategy:

• Structure is Key: For each case study, follow a clear narrative: Problem -> Research Question -> Methodology -> Data Sources -> Analysis -> Key Findings -> Recommendations -> Impact.

• Highlight Your Role: Clearly articulate your specific contributions and ownership within each project.

• Be Data-Centric: Focus on the numbers, the charts, and the statistical significance of your findings. Explain why the data leads to your conclusions.

• Showcase Technical Proficiency: Be prepared to briefly explain the code or statistical models you used, especially if asked.

• Focus on Actionability: Emphasize how your insights translated into tangible product improvements or strategic shifts.

• Practice Your Delivery: Rehearse your presentation to ensure clarity, conciseness, and confidence. Be ready to answer detailed questions about any aspect of your work.

📝 Enhancement Note: Google's interview process heavily emphasizes problem-solving, data-driven decision-making, and cultural fit. Candidates must be able to clearly articulate their thought process, demonstrate technical depth, and show how they drive impact through quantitative insights.

📌 Application Steps

To apply for this Quantitative UX Researcher position:

• Submit your application through the Google Careers portal via the provided link.

• Portfolio Customization: Curate your portfolio to highlight 2-3 of your most impactful quantitative research projects, specifically emphasizing work related to data analysis, statistical modeling, experimental design, and driving product/operational improvements. Ensure case studies clearly demonstrate your role and the measurable outcomes.

• Resume Optimization: Tailor your resume to include keywords from the job description, such as "Quantitative UX Research," "Python," "R," "SQL," "Statistical Analysis," "Experimental Design," "Human-Computer Interaction," and "Log Analysis." Quantify achievements wherever possible (e.g., "Improved user task completion rate by 15% through data-driven design recommendations").

• Interview Preparation: Thoroughly review typical quantitative UX research interview questions, practice explaining your portfolio projects, and prepare to discuss your technical skills (Python, R, SQL, statistics) and problem-solving approach.

• Company Research: Gain a deeper understanding of Google's mission, its Corporate Engineering division, and the "Google for Googlers" philosophy. Consider how your research skills can contribute to optimizing internal systems and employee productivity.

⚠️ 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.

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