• Provides CDM leadership for one or more assigned projects or indications dependent on size and scale of the project. • Takes global accountability and serves as the second line of contact at the project level • Lead studies including (but not limited to) a combination of healthy volunteer and patient populations, multi-site, complex protocol design, strong client management required or reduced timelines. • Ability to organize and effectively prioritize workload and deliverables • Demonstrates leadership and operational expertise in the strategic planning and delivery of CDM deliverables at program and/or project level. • Management and oversight of vendor contracts, resourcing and budget management and oversight of vendor performance for assigned programs, and projects • Communicates and negotiates effectively with all other Program level team members. • Primary point of contact for Clinical Data Management (CDM) • Demonstrates a business understanding of the compound profile to identify and assist in successful application of consistent CDM processes and documentation across assigned programs, (i.e. ensuring consistency across data quality plans.) • Provide oversight and expertise of external service providers or in-house teams to deliver quality data with compliance to study model procedures and standards; give guidance on company standards, processes, systems and expectations to external partners, internal partners and third-party vendors • Responsible for proactive risk management and issue resolution/escalation connected to Clinical Data Management improvement or technology • Develops an understanding of CDASH and SDTM or other recognized industry standards and impact to programming team to ensure consistency of program level standards. • Specialist in TA specific data capture and standards, conducts lessons learned and disseminate across the organization as appropriate • May act as a team leaders or mentor Clinical Data Management colleagues and any stakeholder with operational processes used in studies and projects. • Demonstrates willingness to take on and lead any project level activity consistent with current or experience in support of study delivery.

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.