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.

Role Description We are looking for experienced and ambitious .Net Developer to join our team. As a .NET Developer you will be involved in design, development, coding, customization, configuration, testing, and deployment in support of enterprise packaged solutions. .NET Developer duties and responsibilities - Utilize established development tools, guidelines and conventions including but not limited to ASP.NET, SQL Server, HTML, CSS, JavaScript, and C#/VB.NET - Prepare and maintain code for various .Net applications and resolve any defects in systems. - Prepare test based applications for various .Net applications. - Enhance existing systems by analyzing business objectives, preparing an action plan and identifying areas for modification and improvement - Manage defect tracking system and resolve all issues and prepare update for systems. - Investigate and develop skills in new technologies - Create technical specifications and test plans - Monitor everyday activities of system and provide required support. Qualifications - BS/MS degree in Computer Science, Engineering, MIS, or equivalent with programming experience - X years of design and/or development experience with .NET, C#, ASP.NET, Windows Forms and SQL Server in an n-tier architecture. - Strong background in object oriented development techniques - Solution Design Patterns and Best Practices, e.g. SOA - Database design and development (Microsoft SQL Server) - Experience with Web Services, WCF and Service Oriented Architecture a plus - Experience with concurrent development source control (Git) and continuous integration (Jenkins or Bamboo) - Understanding of Software Development Life Cycle and Agile methodologies. - Passion for technology and knowledge

• Design, build, train, evaluate, and deploy machine learning and generative AI solutions. • Develop and maintain predictive analytics, NLP, computer vision, and LLM-based applications. • Implement Retrieval-Augmented Generation (RAG), agentic workflows, and knowledge management solutions. • Deploy and operate local/open-source models in secure environments. • Design and deploy AI solutions within Azure Government. • Develop data pipelines supporting AI and analytics workloads. • Implement CI/CD pipelines for machine learning and AI workloads. • Collaborate with mission owners, analysts, engineers, cybersecurity personnel, and leadership.

• Lead DTS Development: Author and manage Data Transfer Specifications (DTS) to align external data providers and research partners on required data structures, formats, and templates. • Oversee Data Standardization: Ensure study teams adhere to CDISC standards for external data, including biomarkers, wearable devices, IRT, imaging, and eCOA. • Cross-Functional Collaboration: Serve as the primary contact for external data quality control, participating as an extended member of the Clinical Study Team and facilitating new test codes. • Vendor & Process Management: Provide operational oversight to external data vendors, manage change requests for existing DTS, and contribute to continuous process improvement initiatives. • Data Reconciliation & Compliance: Support data reconciliation, resolve structure inquiries, and comply with all regulatory requirements, including clinical protocols and analysis expectations.