Where we Work

^{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.}

About your skills

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.

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.

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.

About this role

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.

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.

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.

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.

What you’ll be doing

• Partner closely with Enterprise Product, Design, and Engineering teams to use data to inform product strategy, priorities, and roadmap decisions.
• Own the analytical strategy for your product area, including the KPIs and measurement frameworks teams use to understand product performance and customer outcomes.
• Analyze product usage and customer behavior to understand adoption, engagement, retention, user journeys, and friction points, and identify opportunities to improve the product experience.
• Design and evaluate experiments end-to-end, from developing hypotheses and defining metrics to experimental design, analysis, and recommendations.
• Use causal inference and quasi-experimental methods when randomized experiments aren’t feasible to help teams understand the impact of product changes and initiatives.
• Apply statistical and analytical methods, including regression, segmentation, forecasting, and predictive modeling where appropriate, to answer complex product questions.
• Partner with Product and Engineering on instrumentation and measurement strategies so that new and existing product experiences can be reliably evaluated.
• Turn complex analyses into clear recommendations and data stories that help product teams and senior leaders make decisions.
• 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.
• 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.
• 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.

What you’ll have

• Bachelor’s degree in a relevant quantitative or technical field, or equivalent practical experience. An advanced degree is a plus.
• 6+ years of hands-on Data Science experience (4+ years with a PhD), with significant experience partnering directly with product teams and using data to influence product decisions.
• Expert-level SQL and strong proficiency in Python for data analysis, statistics, automation, and modeling.
• Strong applied statistics and experimentation skills, including experimental design, hypothesis testing, power analysis, and A/B testing.
• Experience applying causal inference or quasi-experimental methods to measure impact when randomized experiments aren’t feasible.
• Strong product sense and business intuition, with the ability to take ambiguous product questions and determine how data can best help answer them.
• Experience analyzing user behavior and product engagement through approaches such as funnel, cohort, segmentation, and retention analysis.
• Experience defining product KPIs, measurement frameworks, and instrumentation requirements with Product and Engineering teams.
• Strong data storytelling and visualization skills, with the ability to communicate findings clearly to both technical and non-technical audiences.
• Demonstrated ability to independently scope and lead analytical work, navigate ambiguity and changing priorities, and influence cross-functional stakeholders.
• Strong collaboration and communication skills, with the ability to work effectively with teams across functions and global time zones.

Preferred qualifications

• ^{Experience working with B2B SaaS or Enterprise products, particularly products with multiple user personas, administrators, or organization-level customer outcomes.}

• ^{Experience with product analytics, experimentation, and modern data tools such as Databricks, Amplitude, Statsig, Tableau, Sigma, or similar technologies.}

• ^{Familiarity with data transformation and workflow tools such as dbt and Airflow and software development practices such as Git and code review.}

• ^{Experience using AI/LLM technologies to improve analytical workflows, build data products, or evaluate AI-powered product experiences.}

• ^{Experience in education technology or digital learning products.}

Posting Date:  6/26/26

^{Application Window: We anticipate the application window will be open until 7/3/26. Based on business needs, this opportunity may remain posted beyond or closed before the anticipated application window.}

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