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’re looking for a knowledgeable Data Scientist - Product Analytics to support decision-making for our product teams. This role will be leveraging the rich data captured by over 200M+ 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 Product Managers (PMs) who own parts of Coursera’s educator or learner experience. 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 innovative product development. - Develop an understanding of Coursera’s products, business, and learners to tie insights and analysis to actionable recommendations. - Analyze the results of A/B tests and conduct deep dives on our learner and product experiences. - Create dashboards and set up systems and tools for tracking product metrics. - Create leverage by enabling Product Managers to better self-serve data on their product area. - Present findings and recommendations to product teams and cross-functional partners in a clear and concise manner. Qualifications - Background in economics, statistics, data science, computer science, or a related technical field. - 3+ years of experience as a data scientist, data analyst, or business intelligence analyst. - Strong SQL skills and proficiency with at least one scripting language (e.g. Python, R). - Proficiency in BI tools (e.g. Sigma, Databricks, Looker, Tableau). - Excellent communication skills, with the ability to translate complex data findings into actionable insights. - Strong attention to detail and the ability to manage multiple projects simultaneously. Requirements - 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. - Experience leveraging AI tools to accelerate data analysis, generate insights, or enhance analytical workflows. - Familiarity with online education platforms, specifically with platforms like Coursera and Udemy. Compensation The starting base pay for this role is between $113,600 CAD and $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 is also eligible for bonus, equity, and benefits.

• Learn about Viaduct’s history and mission • Get to know every team member • Set up your development environment • Understand Viaduct’s data pipelines and data science workflow • Learn about our current partners, their biggest needs, and the gaps they're looking for us to fill • Present your first deliverables internally • Scope-out and propose improvements to our products • Deploy improvements to our ML/Agentic pipelines • Present your work at our weekly team meetings • Be a thought leader on the data science team • Deploy world-class DS-based products and services • Collaborate with engineers on improvements to data science tooling • Present your models and relevant metrics to stakeholders

• Develop, validate, and maintain SDTM and ADaM datasets in R — using admiral and the broader pharmaverse — following CDISC standards. • Generate Tables, Listings, and Figures (TLFs) in R or SAS as required by study needs. • Perform Pinnacle21 validation, resolve findings, and refine specifications to ensure CDISC compliance and submission readiness. • Write efficient, reproducible, well-structured R code for clinical data analysis and reporting. • Contribute to open-source and internal tooling — including R Shiny / teal modules, R packages, SAS macros, and Python utilities — used by clinical programming, data management, and biostatistics teams. • Partner with statisticians, data managers, and clinical teams to translate programming requirements into reliable deliverables. • Perform QC, reconcile data issues, and ensure outputs meet regulatory expectations (e.g., FDA, EMA). • Support automation, pipeline development, and version-controlled workflows. • Use SAS for legacy studies or where SAS support is needed.

• Shape the data science roadmap: Identify measurement, audience, and AI capabilities that create value in each specialty market, and build a function that scales with the business. • Build causal lift measurement: Own the framework that demonstrates how HCP engagement drives real-world prescribing change — matched and synthetic controls, lookalike populations, methodologies defensible to pharmaceutical medical affairs and legal, and repeatable across therapeutic areas. • Build decisioning and personalization systems: Develop next-best-action, recommendation, and audience intelligence models that personalize the HCP experience and identify high-value audiences for sponsor targeting at scale. • Apply LLMs and generative AI across the workflow: Use them for structured extraction, feature engineering, insight generation, and HCP profile enrichment — wherever they cut time-to-insight without compromising rigor. • Translate findings into commercial products: Turn model outputs into sponsor-facing measurement reports, audience intelligence packages, and ROI dashboards, and present methodology directly to pharmaceutical medical affairs and commercial teams. • Own the ML platform layer: Feature engineering, experiment tracking, model registry, A/B and holdout frameworks, and production monitoring on the company's cloud and data warehouse stack.