About the 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.
Responsibilities
- 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.
Requirements
- 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.