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At Coursera, our Data Science team is helping to build the future of education through data-driven decision making and data-powered products. We drive product and business strategy through measurement, experimentation, and causal inference to help Coursera deliver effective content discovery and personalized learning at scale. We believe the next generation of teaching and learning should be personalized, accessible, and efficient. With our scale, data, technology, and talent, Coursera and its Data Science team are positioned to make that vision a reality. We are seeking a highly skilled Senior Data Scientist with deep expertise in product experimentation, causal inference, decision science, and machine learning to join our team. In this role, you will be embedded at the intersection of product development and learning science, partnering directly with product managers, engineers, and learning designers to shape how tens of millions of learners experience Coursera. You will bring statistical rigor and a scientist’s mindset to the hardest measurement and modeling problems we face, and your work will directly determine what gets built and why. A strong differentiator for this role is familiarity with learning analytics and/or psychometric methods. You will help us go beyond simple engagement metrics to measure what learners actually know, how they progress, and whether our interventions genuinely improve outcomes. If you are excited by the scientific challenge of measuring learning itself—not just clicks—this role is for you.
Job Responsibility
Experimentation & Causal Inference
Decision Science & Advanced Modeling
Learning Analytics & Psychometrics
Requirements
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
Nice to have
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