Senior UX Data Scientist

📍 Job Overview

Job Title: Senior UX Data Scientist Company: Microsoft Location: Redmond, Washington, United States Job Type: Full-Time Category: Data Science / User Experience Research Date Posted: January 15, 2026 Experience Level: Mid-Senior Level (implied 5-10 years) Remote Status: On-site

🚀 Role Summary

• Leverage advanced data science techniques to drive user experience (UX) strategy and product development within Microsoft's Security organization.
• Analyze complex user behavior patterns through telemetry and instrumentation data to identify opportunities for UX improvement and business impact.
• Collaborate closely with cross-functional teams, including Product Management, Design, Engineering, and other Data Scientists/Analysts, to define research objectives and integrate data-driven insights.
• Develop and expand the UX measurement framework to effectively track user engagement, measure the impact of UX enhancements, and ensure data integrity for strategic decision-making.

📝 Enhancement Note: While the role is titled "Senior UX Data Scientist," the responsibilities and qualifications strongly align with a blend of advanced data science, user research analysis, and strategic product influence. The focus on "UX data science" and "user-level behavioral telemetry" positions this role at the intersection of quantitative analysis and qualitative understanding of user needs, particularly within the security domain. The "Senior" designation implies a need for leadership, mentorship, and the ability to drive significant projects with autonomy.

📈 Primary Responsibilities

• Define and instrument user-level behavioral telemetry across the Microsoft Security product portfolio in collaboration with Data Scientists and UX Researchers.
• Develop robust data collection strategies and statistical analysis plans, prioritizing data integrity and accuracy to support reliable insights.
• Translate complex data findings into actionable recommendations for product managers, designers, and engineers, influencing the design of user-centric security solutions.
• Quantify the effectiveness of UX improvements and user engagement trends by expanding and maintaining the UX measurement framework.
• Conduct in-depth analysis of large datasets using advanced data analytic and data scientific approaches to understand user behavior, identify UX challenges, and uncover critical patterns and trends.
• Partner effectively with cross-functional teams to align research objectives with product development roadmaps and embed data-driven decision-making throughout the product lifecycle.
• Utilize sophisticated analytic tools and techniques to extract meaningful insights from massive datasets, focusing on user behavior and its correlation with product adoption and satisfaction.
• Communicate research findings, insights, and strategic recommendations clearly and persuasively to diverse stakeholders, including senior leadership, through compelling verbal and visual presentations.
• Stay abreast of the latest advancements in UX research, data science, machine learning, and relevant security technologies, applying this knowledge to continuously refine research methodologies and enhance our data-driven approach.

📝 Enhancement Note: The emphasis on "technical and non-technical users of Microsoft Security products" highlights the need for the Senior UX Data Scientist to understand diverse user personas and their specific needs within a complex security ecosystem. This requires not just analytical skills but also a degree of domain understanding and empathy.

🎓 Skills & Qualifications

Education:

• Required: Doctorate or Master's degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or a closely related quantitative field. A Bachelor's degree in one of these fields with extensive relevant experience may also be considered.
• Preferred: Doctorate degree with 3+ years of data science experience, or Master's degree with 5+ years, or Bachelor's degree with 7+ years of progressive data science experience.

Experience:

• Minimum of 1-3 years of data science experience (depending on degree level) in managing structured and unstructured data, applying statistical techniques, and reporting results.
• Proven experience analyzing user experience through dedicated UX research methodologies or UX-focused data science initiatives.
• Demonstrated ability to manage structured and unstructured data, including proficient coding in at least one high-level programming language (e.g., Python, R, C++, C#, Java).

Required Skills:

• Data Science & Analytics: Expertise in statistical analysis, data mining, and the application of machine learning and deep learning algorithms.
• User Experience Focus: Proven ability to analyze user experience data, understand user behavior patterns, and translate these into actionable UX insights.
• Programming Proficiency: Strong coding skills in languages like Python or R for data manipulation, analysis, and modeling.
• Data Integrity & Collection: Experience in defining data collection strategies and ensuring data integrity for robust analysis.
• Collaboration & Communication: Excellent ability to collaborate effectively with cross-functional teams (PM, Design, Engineering) and present complex findings clearly to both technical and non-technical audiences.

Preferred Skills:

• Advanced ML/DL: Deep expertise in advanced algorithms, machine learning, and deep learning techniques.
• Experimentation: Proficiency in data mining, big data technologies, and experimentation methods, particularly A/B testing.
• Data Storytelling: Demonstrated ability to influence stakeholders and communicate compelling data narratives.
• Agile Methodologies: Experience working within Agile development environments.
• Data Visualization & Dashboards: Experience building dashboards using tools such as Power BI, Tableau, or similar platforms.
• Research Methodologies: Familiarity with a broad range of UX research methodologies.

📝 Enhancement Note: The experience requirements clearly indicate a need for a senior-level candidate who can operate independently and lead complex analytical projects. The emphasis on both a strong theoretical foundation (degree requirements) and practical application (years of experience, coding, specific techniques) is critical. The dual requirement of "UX research OR UX-focused data science" suggests flexibility but a strong preference for candidates who can bridge these disciplines.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Data-Driven UX Case Studies: Showcase projects where you used data analysis to identify UX issues, propose solutions, and measure the impact of those solutions on user behavior and key performance indicators.
• Telemetry Definition & Analysis: Examples of how you've contributed to defining behavioral telemetry, designing data collection strategies, and analyzing resulting large datasets.
• Statistical Modeling & Machine Learning: Demonstrate your ability to apply statistical techniques and machine learning models to understand complex user behaviors and predict outcomes.
• Cross-Functional Collaboration Examples: Highlight instances where you successfully partnered with designers, product managers, and engineers, translating data insights into product improvements.
• Impact Measurement: Evidence of how your work has directly influenced product decisions, improved user engagement, or driven business objectives, quantified with metrics.

Process Documentation:

• Workflow Design & Optimization: Document processes you've implemented or optimized for data collection, analysis, and reporting related to user experience.
• Measurement Framework Development: Showcase your contributions to building or enhancing frameworks for measuring UX effectiveness and user engagement.
• Data-Driven Decision Making: Illustrate processes you've established or influenced to ensure data insights are consistently integrated into product development cycles.

📝 Enhancement Note: For a Senior UX Data Scientist role, a strong portfolio is paramount. It needs to demonstrate not just analytical prowess but also the ability to translate complex data into actionable UX insights and drive tangible product improvements. Candidates should be prepared to discuss the "why" and "how" behind their projects, focusing on the impact and lessons learned.

💵 Compensation & Benefits

Salary Range:

• US National Range (excluding specific high-cost areas): $119,800 - $234,700 USD per year (Base Pay).
• San Francisco Bay Area & New York City: $158,400 - $258,000 USD per year (Base Pay).

Benefits:

• Comprehensive health, dental, and vision insurance plans.
• Retirement savings plans (e.g., 401(k) with company match).
• Paid time off, including vacation, sick leave, and holidays.
• Parental leave and family support benefits.
• Employee Assistance Program (EAP).
• Opportunities for professional development, training, and certifications.
• Stock purchase programs or equity options (may vary by role/level).
• Wellness programs and resources.
• Relocation assistance (if applicable).

Working Hours:

• Standard full-time hours, typically 40 hours per week.
• Flexibility may be offered, but the role is designated as On-site, implying regular in-office presence is expected.

📝 Enhancement Note: The provided salary ranges are base pay only. Microsoft typically offers a total compensation package that includes base salary, bonuses (performance-based), and stock awards, which can significantly increase the overall remuneration. The specific benefits package is comprehensive and competitive, aligning with large tech organizations.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology (Software, Cloud Services, Security) Company Size: Microsoft is a large, multinational technology corporation with hundreds of thousands of employees globally. This implies a highly structured environment with extensive resources, established processes, and opportunities for specialization. Founded: 1975. Microsoft has a long history of innovation and market leadership, particularly in operating systems, productivity software, and increasingly, cloud computing and security.

Team Structure:

• The role is within the Microsoft Security organization, specifically a team comprising Product Designers, UX Researchers, Data Scientists, Content Designers, and Technical Writers.
• This team operates in close partnership with Product Management (PM) and Engineering teams.
• The Senior UX Data Scientist will likely report into a Data Science or UX Research leadership role, with significant dotted-line reporting or collaboration with PM and Design leads.

Methodology:

• Data-Driven Product Development: A core tenet is using data telemetry and user insights to inform and validate product design decisions.
• User-Centric Design: Emphasis on human-centered design principles to create intuitive, trustworthy, and easy-to-use product experiences.
• Growth Mindset: Encouragement of continuous learning, embracing challenges, and fostering innovation.
• Agile Development: Likely adherence to Agile methodologies for iterative product development and rapid response to user feedback and market changes.

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

📝 Enhancement Note: Microsoft's security division is a critical and high-priority area, indicating that this role will be involved in impactful work addressing significant customer needs in a rapidly evolving threat landscape. The company's scale means that even a specialized role can influence millions of users globally.

📈 Career & Growth Analysis

Operations Career Level: This is a "Senior" individual contributor (IC) role, likely equivalent to an IC4 level within Microsoft's grading system. It requires significant technical expertise, a proven track record, and the ability to lead projects and influence strategy. Reporting Structure: The role reports into a manager within the Data Science or UX Research function, but will work extensively with cross-functional leads (PM, Design) on specific product initiatives. This matrixed structure is common in large tech organizations. Operations Impact: The Senior UX Data Scientist's impact is measured by their ability to translate complex user data into actionable insights that directly improve the user experience of Microsoft Security products. This, in turn, is expected to drive increased user adoption, satisfaction, retention, and ultimately, business success for the security offerings.

Growth Opportunities:

• Technical Specialization: Deepen expertise in advanced machine learning, statistical modeling, and specific areas of UX data science within the security domain.
• Leadership Development: Transition into technical lead roles, mentoring junior data scientists, or managing complex, cross-team initiatives.
• Cross-Functional Mobility: Potential to move into related roles in Product Management, Program Management, or broader Data Science/Analytics teams within Microsoft.
• Domain Expertise: Develop deep knowledge of the cybersecurity landscape and its impact on user experience.
• Strategic Influence: Grow to influence long-term product strategy and research agendas for the Security organization.

📝 Enhancement Note: The "Senior" title implies a pathway to Principal or Lead Data Scientist roles, or potentially management tracks, depending on career aspirations. The emphasis on "influencing impactful UX projects and long-term strategic explorations" suggests significant autonomy and a high level of expected contribution.

🌐 Work Environment

Office Type: On-site role at Microsoft's Redmond, Washington campus. This suggests a large, modern corporate campus environment designed for collaboration and innovation. Office Location(s): Redmond, Washington, USA. This is Microsoft's global headquarters, offering extensive amenities and a concentrated hub of talent.

Workspace Context:

• Collaborative Spaces: The campus likely features a mix of open-plan work areas, private offices, meeting rooms, and informal collaboration zones to facilitate interaction.
• Technology & Tools: Access to Microsoft's extensive internal technology stack, high-performance computing resources, and advanced data analytics platforms.
• Team Interaction: Regular opportunities for face-to-face interaction with immediate team members, cross-functional partners (PM, Design, Engineering), and other data science professionals across the organization.

Work Schedule: Standard 40-hour work week with potential for some flexibility, though the on-site requirement means consistent presence in the Redmond office is expected.

📝 Enhancement Note: Working on the Microsoft campus provides access to a vibrant ecosystem of professionals, resources, and development opportunities. The environment is typically geared towards innovation and collaboration, with a strong emphasis on leveraging technology to solve complex problems.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: Recruiter call to assess basic qualifications, interest, and cultural fit.
• Hiring Manager Interview: Deeper dive into experience, technical skills, and alignment with the role's responsibilities. May include behavioral questions.
• Technical Phone Screen/Online Assessment: Assessment of core data science skills, coding proficiency, and statistical knowledge.
• On-site (or Virtual On-site) Loop: Typically involves multiple interviews (4-6) covering:
  • Data Science/ML: Problem-solving, algorithm design, statistical concepts.
  • UX Data Science: Applying data to understand user behavior, defining metrics, analyzing UX impact.
  • Coding/Technical: Practical coding challenges, data manipulation, and algorithm implementation.
  • Behavioral/Situational: Assessing collaboration, communication, leadership, and problem-solving approaches.
  • Portfolio Presentation: A dedicated session to present previous work and case studies.
• Final Review: Discussion among interviewers to reach a consensus and extend an offer.

Portfolio Review Tips:

• Curate Select Projects: Choose 3-4 impactful projects that best showcase your skills in UX data science, statistical analysis, and cross-functional collaboration.
• Structure Your Narrative: For each project, clearly articulate the problem, your role, the data/methods used, the insights derived, the actions taken based on those insights, and the measurable impact.
• Quantify Everything: Use metrics and data to demonstrate the success of your work. Show how you measured UX improvements or user behavior changes.
• Highlight Collaboration: Emphasize how you partnered with designers, PMs, and engineers, and how your data insights facilitated their work.
• Prepare for Deep Dives: Be ready to discuss the technical details of your analyses, the assumptions made, and alternative approaches you considered.
• Tailor to Microsoft Security: If possible, subtly link your examples to security contexts or user challenges relevant to the domain.

Challenge Preparation:

• Coding Practice: Brush up on Python/R, data structures, algorithms, and SQL. LeetCode (medium/hard) or similar platforms are good practice.
• Statistical Concepts: Review probability, hypothesis testing, regression, experimental design (A/B testing).
• ML/DL Fundamentals: Understand common algorithms (e.g., classification, regression, clustering), model evaluation, and use cases.
• UX Data Science Scenarios: Practice analyzing hypothetical user data, defining key metrics for user engagement, or diagnosing UX issues based on simulated telemetry.
• Behavioral Questions: Prepare STAR method (Situation, Task, Action, Result) responses for common questions about teamwork, problem-solving, and leadership.

📝 Enhancement Note: The interview process at Microsoft is rigorous. For this role, demonstrating a strong connection between technical data science skills and their application to improving user experience is critical. The portfolio presentation is a key opportunity to showcase this connection.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: Python (highly probable, with libraries like Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch), R.
• Data Analysis & Manipulation: SQL for querying large databases, potentially Spark for big data processing.
• Machine Learning Frameworks: Scikit-learn, TensorFlow, PyTorch, Keras.
• Statistical Software: Potentially R packages or specialized statistical libraries.

Analytics & Reporting:

• Business Intelligence/Dashboarding: Power BI (Microsoft's native tool, highly likely), Tableau, or similar visualization tools.
• Data Warehousing: Experience with large-scale data warehousing solutions common in enterprise environments.

CRM & Automation:

• While not a CRM role, understanding how user data integrates with product usage platforms and potentially customer feedback systems is beneficial.
• Experimentation Platforms: Tools for managing and analyzing A/B tests and other experiments.

📝 Enhancement Note: Given Microsoft's ecosystem, proficiency with Power BI is a strong advantage. Familiarity with Azure services (e.g., Azure Databricks, Azure ML) would also be highly relevant for data processing and model deployment. The ability to work with "very large data sets" implies experience with distributed computing frameworks.

👥 Team Culture & Values

Operations Values:

• Customer Focus: Deep commitment to understanding and serving customer needs, ensuring products are valuable and easy to use.
• Data-Driven Decisions: A strong emphasis on using data and evidence to guide strategy and product development, moving beyond intuition.
• Collaboration & Inclusion: Fostering an environment where diverse perspectives are valued, and teamwork is essential for success. Microsoft actively promotes a culture of respect and inclusion.
• Innovation & Growth Mindset: Encouraging experimentation, learning from failures, and continuously seeking new and better ways to solve problems.
• Accountability: Taking ownership of projects and delivering high-quality results.

Collaboration Style:

• Cross-Functional Integration: Expect close collaboration with PM, Design, and Engineering teams, requiring strong communication and negotiation skills.
• Data Sharing & Transparency: Encouraging the sharing of data insights across teams to foster a unified understanding of user behavior.
• Iterative Feedback Loops: Working within Agile frameworks means continuous feedback exchange and adaptation based on new data and insights.
• Knowledge Exchange: Opportunities to share learnings and best practices within the broader data science and UX research communities at Microsoft.

📝 Enhancement Note: Microsoft's stated values of Respect, Integrity, and Accountability, coupled with a Growth Mindset, are foundational to its culture. For a Senior UX Data Scientist, demonstrating how these values translate into daily work—e.g., ethical data handling, collaborative problem-solving, seeking constructive feedback—will be important.

⚡ Challenges & Growth Opportunities

Challenges:

• Complexity of Security Domain: Navigating the intricate and rapidly evolving landscape of cybersecurity threats and user needs within a complex product suite.
• Data Scale & Quality: Working with massive datasets that may have inherent quality issues or require sophisticated processing to extract meaningful signals.
• Translating Insights to Action: Bridging the gap between complex data analysis and actionable product design changes, especially in a large organization.
• Cross-Functional Alignment: Ensuring buy-in and consistent application of data-driven insights across diverse teams with potentially different priorities.
• Measuring Subtle UX Impacts: Quantifying the impact of UX improvements in a domain where user trust and security are paramount, and direct behavioral metrics can be nuanced.

Learning & Development Opportunities:

• Advanced Training: Access to internal and external training on cutting-edge machine learning techniques, statistical methods, and UX research practices.
• Industry Conferences: Opportunities to attend and present at leading data science, analytics, and user experience conferences.
• Mentorship: Potential to be mentored by senior leaders or to mentor junior team members, fostering leadership skills.
• Specialized Certifications: Pursuing certifications in cloud platforms (Azure), data science, or specific analytical tools.
• Internal Knowledge Sharing: Engaging with Microsoft's extensive internal documentation, forums, and communities of practice for continuous learning.

📝 Enhancement Note: The challenges presented are typical for senior roles in large tech companies, particularly within specialized domains like security. The growth opportunities highlight Microsoft's commitment to employee development and career progression.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you used data to significantly improve a user experience. What was the problem, your approach, the outcome, and what did you learn?" (Focus on STAR method, quantify impact, highlight UX connection).
• "How would you define telemetry requirements for a new feature in a security product? What key behaviors would you want to track, and why?" (Demonstrate understanding of data collection strategy and user behavior modeling).
• "Imagine you find a statistically significant correlation between a specific user action and increased product churn. How would you investigate this further to determine causality and propose solutions?" (Showcase analytical rigor, hypothesis testing, and problem-solving approach).
• "How do you ensure the data you use for UX analysis is accurate and representative? What steps do you take to mitigate bias?" (Focus on data integrity, quality checks, and awareness of potential pitfalls).

Company & Culture Questions:

• "Why are you interested in Microsoft Security specifically, and how do you see your skills contributing to our mission?" (Research Microsoft's security initiatives and align your motivations).
• "Describe a challenging cross-functional collaboration you experienced. How did you navigate it, and what was the result?" (Highlight communication, negotiation, and teamwork skills).
• "How do you stay current with advancements in data science and UX research?" (Showcase continuous learning and passion for the field).
• "What are your thoughts on the ethical implications of collecting and analyzing user data for product improvement?" (Demonstrate awareness of privacy and ethical considerations).

Portfolio Presentation Strategy:

• Lead with Impact: Start with the most significant outcomes and business value.
• Tell a Story: Weave a narrative around your projects, explaining the journey from problem identification to solution and impact.
• Be Data-Centric: Clearly present the data, your analytical methods, and the resulting insights. Use visuals effectively.
• Emphasize UX Connection: Explicitly connect your data analysis to user behavior, pain points, and improvements.
• Showcase Collaboration: Discuss how you worked with other teams and incorporated their feedback.
• Be Ready for Q&A: Anticipate deep technical questions about your methodologies and business questions about the impact.

📝 Enhancement Note: Preparation should focus on demonstrating a strong analytical foundation combined with a deep understanding of user experience principles and the ability to drive product strategy through data. The interviewers will be looking for a candidate who can not only perform complex analyses but also translate them into business value and influence product direction.

📌 Application Steps

To apply for this Senior UX Data Scientist position:

• Submit your application through the official Microsoft Careers portal via the provided URL.
• Portfolio Customization: Curate your resume and portfolio to highlight experience in UX data science, statistical analysis, machine learning, and cross-functional collaboration, with specific examples relevant to user behavior and product improvement.
• Resume Optimization: Ensure your resume clearly details your educational background, years of experience, technical skills (programming languages, tools), and quantifiable achievements in data science and UX analysis. Use keywords from the job description.
• Interview Preparation: Practice answering common data science, behavioral, and UX-focused interview questions. Prepare your portfolio presentation, focusing on clear storytelling and quantifiable impact.
• Company Research: Familiarize yourself with Microsoft's Security mission, recent product announcements, and company culture, particularly its emphasis on a growth mindset and data-driven decision-making.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions tailored for a Senior UX Data Scientist role at Microsoft. All details should be verified directly with the hiring organization during the application and interview process.