Staff Data Scientist

About the company

Udemy is an online skills-focused marketplace where instructors create and sell user-generated courses for consumers and Udemy for Business clients, with course creation tools and multilingual global delivery.

About this role

<div class="content-intro"><p><strong>About Coursera + Udemy</strong></p> <p>Coursera and Udemy are now one company, bringing together two mission-driven brands to create the world’s most powerful platform for turning learning into progress. Together, we help more than 300 million learners and 12,000+ enterprise customers build the skills they need for a world being reshaped by AI.</p> <p><strong>Why join us now?</strong></p> <p>AI is transforming how people learn, work, and grow, and the need for new skills has never been greater. Coursera brings trusted content and credentials from leading university and industry partners, while Udemy brings a dynamic skills marketplace and global network of real-world experts. By combining these strengths, we can connect more people and organizations to the skills they need, when they need them.</p> <p><strong>Shape what comes next</strong></p> <p>By joining our team, you’ll have the opportunity to reshape how the world learns and applies skills—and help millions of people participate in the new economy. Bring your ideas, expertise, and perspective to meaningful work that can make a difference at global scale.</p></div><h3><strong>Where we Work</strong></h3> <p><span style="font-size: 12pt;">^{Udemy is a global company headquartered in San Francisco, with additional U.S. offices in Denver and Austin, and international hubs in Australia, India, Ireland, Mexico, and Türkiye.}</span></p> <h3><strong>About your skills</strong></h3> <p>You’re a data scientist with strong product instincts who enjoys using data to understand how customers use products and what we should build next. You’re comfortable taking an ambiguous product question, figuring out the right analytical approach, and turning your findings into clear recommendations for product teams.</p> <p>You’re fluent in SQL, Python, experimentation, and applied statistics. You know when a straightforward analysis is enough and when a problem calls for more advanced methods. You partner naturally with product managers, designers, and engineers, and are as comfortable contributing to a roadmap discussion as you are digging into the data.</p> <p>You care about doing rigorous work, but also about making that work useful. You look for ways to improve how teams measure product success, run experiments, and use data to make decisions. You’re also curious about emerging AI technologies and how they can improve our products and the way we work.</p> <p><strong>About this role</strong></p> <p>This is a highly visible role on our Enterprise Product Data Science team, partnering with product, design, and engineering teams across the combined Coursera and Udemy Enterprise business.</p> <p>As a Staff Data Scientist, you will help Enterprise Product teams understand how customers use our products and identify opportunities to make those products better. You’ll help define how we measure product success, design and evaluate experiments, conduct deep-dive analyses, and turn what we learn about customer behavior into recommendations that influence product strategy and roadmaps.</p> <p>Our Enterprise products serve different users, from learners and administrators to organizations. You’ll help us understand how these different users engage with our products, what drives adoption and engagement, and how product experiences ultimately create value for our customers.</p> <p>The specific product area supported by this role may evolve as our Enterprise portfolio and priorities develop across Coursera and Udemy. Success in this role will require strong product sense, analytical rigor, communication and collaboration skills, and a customer-centric mindset.</p> <p><strong>What you’ll be doing</strong></p> <ul> <li>Partner closely with Enterprise Product, Design, and Engineering teams to use data to inform product strategy, priorities, and roadmap decisions.</li> <li>Own the analytical strategy for your product area, including the KPIs and measurement frameworks teams use to understand product performance and customer outcomes.</li> <li>Analyze product usage and customer behavior to understand adoption, engagement, retention, user journeys, and friction points, and identify opportunities to improve the product experience.</li> <li>Design and evaluate experiments end-to-end, from developing hypotheses and defining metrics to experimental design, analysis, and recommendations.</li> <li>Use causal inference and quasi-experimental methods when randomized experiments aren’t feasible to help teams understand the impact of product changes and initiatives.</li> <li>Apply statistical and analytical methods, including regression, segmentation, forecasting, and predictive modeling where appropriate, to answer complex product questions.</li> <li>Partner with Product and Engineering on instrumentation and measurement strategies so that new and existing product experiences can be reliably evaluated.</li> <li>Turn complex analyses into clear recommendations and data stories that help product teams and senior leaders make decisions.</li> <li>Develop reusable analytical frameworks and tools that make it easier for product teams to answer recurring questions and use data in their day-to-day decisions.</li> <li>Partner with Data Engineering and Analytics Engineering on instrumentation, data models, and other data foundations needed for reliable product analytics, while being comfortable self-serving when needed to move an analysis forward.</li> <li>Raise the analytical bar across the team by reviewing analytical and experimental approaches, mentoring other data scientists, and contributing to shared tools and best practices.</li> </ul> <p><strong>What you’ll have</strong></p> <ul> <li>Bachelor’s degree in a relevant quantitative or technical field, or equivalent practical experience. An advanced degree is a plus.</li> <li>6+ years of hands-on Data Science experience (4+ years with a PhD), with significant experience partnering directly with