• Get insight from an enormous amount of data, and take a proactive role partnering with the marketing team in finding and testing data-driven ideas for improving our efficiency. • Contribute to the development, implementation, and maintenance of our marketing models, including a Bayesian Marketing Mix Model and a Multi-Touch Attribution model. • Monitor and analyze marketing uplift tests with statistical rigor, present the data and insights to the team, and drive marketing decisions. • Look at complex problems and come up with testable models and algorithms that have measurable business impact. • Enjoy great teamwork, have lots of fun, and take pride in building a world-class product that makes a difference in people's lives.

• Explore large-scale event data to identify trends, patterns, and anomalies • Generate insights/ideas, evaluate campaigns’ performance • Support decision-making based on findings • Design, implement, optimize and test algorithms for CPA optimization • Build and refine predictive models to improve decisions for CPA campaigns • Ensure alignment with business objectives • Work closely with product/business/tech teams to turn technical findings into actionable decisions • Conduct and evaluate A/B tests to assess the impact of algorithm changes • Create dashboards and visualizations to track key performance indicators (KPIs) and communicate findings to stakeholders

Role Description 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’re looking for an experienced Data Scientist to support decision-making for our content-making partners and innovate how AI tools can help us do this more efficiently. This role will be leveraging the rich data captured over a hundred million learners and thousands of instructors engaging on the platform to deliver innovative, data-driven insights on how to create experiences that will better meet the needs and expectations of learners. This role will partner closely with Account managers who own the relationship with our content partners and are responsible for helping them grow. Our ideal candidate possesses strong analytical and dashboarding skills, familiarity with A/B testing, good product sense, and the ability to translate analysis into actionable recommendations that drive product and business outcomes. Responsibilities - Create dashboards for tracking business metrics. - Analyze the results of A/B tests on our product experience. - Develop an understanding of Coursera’s products, business, and learners to tie analysis to actionable recommendations. - Create leverage by enabling stakeholders to better self-serve data on their product area. - Present findings and recommendations to leadership in a clear and concise manner. Qualifications - Background in economics, statistics, data science, computer science, or a related technical field. - 2+ years of experience as a data scientist, data analyst, or business intelligence analyst. - Proficiency in BI tools (e.g. Sigma, Databricks, Looker, Tableau). - Strong SQL skills and proficiency with at least one scripting language (e.g. Python, R). - Excellent communication skills, with the ability to translate complex data findings into actionable insights. Preferred Qualifications - Experience leveraging AI tools to accelerate data analysis, generate insights, or enhance analytical workflows. - 5+ years of experience using data to advise product, marketing, or business teams. - Advanced proficiency with SQL and Python to produce clear and actionable insights. - Familiarity with online education platforms, specifically with platforms like Coursera and Udemy. Compensation Our job titles may span more than one career level. The starting base pay for this role is between $120,000 CAD to $142,000 CAD. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs, and location. The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, and benefits.

Role Description 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. Responsibilities - Experimentation & Causal Inference - 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. - Decision Science & Advanced Modeling - 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. - Learning Analytics & Psychometrics - 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. Qualifications - 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. Preferred Qualifications - 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. Compensation Coursera offers competitive pay and fair compensation practices across all regions. Job titles may span multiple career levels, and the targeted hiring base salary range for this role in Canada is $137,600 CAD to $172,000 CAD. Actual compensation will depend on factors such as experience, education, transferable skills, business needs, and location. This range may be adjusted over time and may include eligibility for variable pay, equity, and comprehensive benefits.