Senior Data Scientist
Location
Canada
Posted
3 days ago
Salary
C$137.6K - C$172K / year
Seniority
Senior
Job Description
Senior Data Scientist
Coursera Sourcing
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.
Related Guides
Related Categories
Related Job Pages
More Data Scientist Jobs
Role Description Make an impact while you learn. The Semester of Service Program offers students a volunteer project-based opportunity to support real Federal missions, gaining hands-on experience and career-ready skills. Students must be enrolled at least half-time in an accredited trade school, technical/vocational institute, junior college, college, university, or other accredited educational institution. With this project, you'll support a program through research and data analytics. Qualifications - Strong interest in Natural or Agricultural Sciences. - Knowledge, skills, or abilities related to project needs. Requirements - Must be enrolled not less than half-time in an accredited trade school, technical or vocational institute, junior college, college, university, or other accredited educational institution. - Must be in good academic standing as defined by your institution. - Attach a copy of your transcripts to your application package for verification.
Role Description Make an impact while you learn. The Semester of Service Program offers students a volunteer project-based opportunity to support real Federal missions, gaining hands-on experience and valuable career-ready skills. Students must be enrolled not less than half-time in an accredited trade school, technical or vocational institute, junior college, college, university, or other accredited educational institution. With this project, you'll support a data science based project. Qualifications - Applicants will be considered based on their knowledge, skills or abilities related to project needs. - Specifically, applicants should have experience in: - Python - R - Git - SAS - Basic statistics - Data Processing Requirements - To qualify, you must be enrolled not less than half-time in an accredited trade school, technical or vocational institute, junior college, college, university, or other accredited educational institution. - You also must be in good academic standing as defined by your institution. - Attach a copy of your transcripts to your application package for verification.
Data Scientist
Weekday (YC W21)We are a Y-Combinator-backed startup building your AI-powered Recruiter Agent
• Transform complex distributor transaction data into actionable sales opportunities. • Develop predictive models, recommendation engines, and commercial analytics solutions that directly influence sales strategies and revenue growth. • Work with large-scale B2B datasets containing thousands of customers, products, SKUs, and branch locations. • Turn raw ERP data into meaningful insights, dashboards, and opportunity recommendations for commercial teams and business leaders. • Support sales teams, executive decision-making, and business growth initiatives.
Data Scientist
Weekday (YC W21)We are a Y-Combinator-backed startup building your AI-powered Recruiter Agent
Role Description We are seeking an experienced Data Scientist to transform complex distributor transaction data into actionable sales opportunities. This role focuses on developing predictive models, recommendation engines, and commercial analytics solutions that directly influence sales strategies and revenue growth. You will work with large-scale B2B datasets containing thousands of customers, products, SKUs, and branch locations, turning raw ERP data into meaningful insights, dashboards, and opportunity recommendations for commercial teams and business leaders. This is a highly impactful role where your work will directly support sales teams, executive decision-making, and business growth initiatives. Key Responsibilities - Advanced Analytics & Machine Learning - Build and optimize cross-sell recommendation engines using market basket analysis, affinity modeling, and peer-based recommendation techniques. - Develop predictive models for: - Product reorder forecasting - Customer churn and retention analysis - Customer lifetime value (CLV) - Lapsed product detection - White-space opportunity identification - Share-of-wallet estimation - Create customer segmentation frameworks to improve sales targeting and account prioritization. - Commercial & Sales Analytics - Conduct sales performance analysis, pricing optimization studies, and customer coverage assessments. - Generate actionable account-level insights that help sales teams identify growth opportunities. - Develop data-driven recommendations that support executive reporting and strategic decision-making. - Data Engineering & Data Quality - Work extensively with ERP-sourced transactional data, including: - Customer master data - Product hierarchies - Invoice-level transactions - Branch and location structures - Clean, standardize, and transform large, complex datasets from multiple sources. - Address challenges such as duplicate records, inconsistent hierarchies, and incomplete data. - Business Intelligence & Visualization - Translate analytical outputs into user-friendly dashboards and reports. - Deliver insights through visualization tools such as Power BI, Tableau, or custom reporting solutions. - Ensure outputs are easily consumable by business users and sales teams. - Stakeholder Communication - Present findings, recommendations, and model outcomes to business leaders and non-technical stakeholders. - Explain complex analytical concepts in clear, practical business language. - Support executive-level discussions with data-backed insights and recommendations. Success Metrics - First 90 Days: Deliver an end-to-end customer opportunity model from raw data ingestion through actionable sales output. - Within 6 Months: Build standardized and reusable recommendation methodologies applicable across multiple business scenarios. - Within 12 Months: Develop scalable analytics frameworks and reusable data science solutions that can be deployed across multiple client environments. Qualifications - 5–9 years of experience in Data Science, Analytics, or Machine Learning roles. - Strong experience working with B2B commercial datasets involving customers, products, transactions, and sales data. - Proven experience developing: - Recommendation systems - Market basket analysis models - Propensity and predictive analytics models - Advanced proficiency in Python and SQL. - Experience handling large-scale transactional datasets with millions of records and extensive product catalogs. - Hands-on experience working with ERP-generated data and complex commercial data structures. - Strong analytical thinking and problem-solving capabilities. - Ability to communicate technical concepts effectively to business stakeholders. Preferred Qualifications - Experience with ERP platforms such as Prophet 21, Eclipse, Kinetic, or similar enterprise systems. - Background in distribution, wholesale, manufacturing, industrial products, retail analytics, or B2B commerce. - Experience building analytics solutions around SKU-level purchasing behavior. - Knowledge of recommendation engines and customer analytics in B2B environments. - Hands-on experience with Power BI, Tableau, or similar visualization tools. - Familiarity with data quality challenges including duplicate records, inconsistent hierarchies, and fragmented transactional data. Key Skills - Must-Have Skills: - Data Science - Machine Learning - Python - SQL - ERP Data Analytics - Recommendation Systems - Predictive Modeling - B2B Analytics - Good-to-Have Skills: - Market Basket Analysis - Customer Segmentation - Power BI - Tableau - Distribution & Wholesale Analytics - Commercial Intelligence - Sales Analytics What Makes This Opportunity Unique - Work on high-impact analytics initiatives that directly influence sales outcomes and business growth. - Gain exposure to executive-level decision-making and strategic commercial initiatives. - Build scalable analytics products and frameworks with real-world business applications. - Operate in a highly autonomous, data-driven environment with significant ownership and visibility.
