Senior Data Scientist

Coursera

3h•CA$137,600 - CA$172,000

About The Position

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.

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 Haves

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.

Responsibilities

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.