Senior Data Scientist

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. Read more about the combined company by visiting our blog.

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.

• Design, execute, and analyze A/B and multivariate experiments to evaluate product changes, learning interventions, and personalization strategies.
• Apply causal inference techniques (e.g., difference-in-differences, instrumental variables, regression discontinuity) where randomized experiments are not feasible.
• Develop robust frameworks for measuring treatment effects, handling interference, and addressing novelty/primacy effects in experimentation.
• Partner with product and engineering teams to define success metrics, set experiment guardrails, and ship decisions with confidence.

• Build statistical and ML models to support product roadmap decisions, learner segmentation, and personalization at scale.
• Apply predictive modeling, survival analysis, and Bayesian inference to understand learner behavior and forecast outcomes.
• Develop decision frameworks that weigh trade-offs across multiple business and learning objectives.
• Leverage GenAI tools and automation agents to accelerate analysis workflows and scale insight generation.

• Apply psychometric methods (e.g., item response theory, latent variable models, reliability and validity analysis) to measure learning outcomes and assessment quality.
• Design and evaluate instrumentation strategies that capture meaningful signals of learner knowledge and progress—not just activity.
• Partner with curriculum and learning design teams to define and operationalize constructs like mastery, engagement, and skill acquisition.
• Design and implement instrumentation strategies for accurate tracking of user interactions and data collection.

• Bachelor’s or Master’s degree (or PhD) in Economics, Statistics, Computer Science, Cognitive Science, Psychometrics, Educational Measurement, or a related quantitative field.
• 7+ years of experience applying data science to product or business problems, with a strong track record of influencing decisions through rigorous analysis.
• Expert-level SQL and advanced Python proficiency, including fluency with data manipulation libraries (Pandas, NumPy) and scientific computing (SciPy, Statsmodels, scikit-learn).
• Deep applied statistics background: statistical inference, hypothesis testing, causal inference, Bayesian methods, and experimental design.
• Demonstrated experience designing and analyzing controlled experiments (A/B tests) at scale, including power analysis, sequential testing, and dealing with violations of standard assumptions.
• Experience with ML modeling in production contexts: feature engineering, model validation, bias-variance trade-offs, and model monitoring.
• Strong command of data visualization and the ability to translate complex statistical findings into clear, compelling narratives for non-technical audiences.
• Excellent written and verbal communication; comfortable presenting to senior leadership and cross-functional stakeholders.

• Graduate study of psychometric modeling, item response theory (IRT), latent trait models, or educational measurement in a research or applied context.
• Familiarity with learning analytics frameworks: measuring knowledge acquisition, skill development, or learner progression in digital environments.
• Experience applying causal inference methods beyond A/B testing (e.g., synthetic control, propensity score matching, uplift modeling).
• Background in the educational technology sector, specifically with large-scale online learning environments.
• Experience with Airflow, Databricks, and/or Looker for pipeline orchestration and self-serve analytics.
• Experience with Amplitude or equivalent product analytics platforms.
• Exposure to survival analysis, time-series forecasting, or longitudinal data modeling.

If this opportunity interests you, you might like these courses on Coursera:

• Go Beyond the Numbers: Translate Data into Insights
• Applied AI with DeepLearning
• Probability & Statistics for Machine Learning & Data Science