• Own and evolve CX's Downstream Impact of Support (DSI) revenue calibration models, translating support interaction data into quantified revenue signals. • Design and execute causal inference frameworks and experiments to measure the incremental impact of CX programs (Concierge, Proactive Outreach, automation interventions) on customer retention and product engagement. • Build and maintain LLM-powered classification pipelines for CX contact taxonomy, customer friction detection, and issue attribution, partnering with Analytics Engineers to productionize models into CX's governed Source of Truth infrastructure. • Partner with CX Program Managers and Product teams to define segmentation models and behavioral signals that enable personalized experiences and improve business outcomes. • Maintain a high bar for statistical rigor across CX's analytics function, ensuring experimentation, causal analyses, and model outputs meet the standards required for executive reporting and regulatory defensibility.

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. - 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. Requirements - 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.

• Understand key issues in agriculture and aquaculture and supply chains and metrics for assessing supply chain sustainability • Clean, integrate and analyse structured and unstructured supply chain data from multiple sources, including farms, factories, IoT sensors, ERP systems, open-source data and FAI’s custom-built applications • Collaborate with FAI’s consultants and industry experts to define and solve data problems • Conduct exploratory data analysis (EDA) to surface trends, patterns, and anomalies • Create clear visualizations and decision-support dashboards to communicate findings to both technical and non-technical stakeholders • Translate findings into practical recommendations that clients and industry can act on • Build and validate predictive models and machine learning algorithms • Document methodologies, code, and results and present findings to ensure reproducibility and shared understanding • Stay current with developments in data science, ML, and relevant industry trends

• Lead the planning, execution, and delivery of complex data science and mathematics projects • Provide technical leadership and mentorship to junior and mid-level team members • Collaborate with stakeholders to define project requirements, scope, and deliverables • Identify risks, develop mitigation strategies, and ensure the successful completion of projects • Design and implement advanced statistical, predictive, and machine learning models • Perform sophisticated exploratory data analysis (EDA) to identify actionable insights • Develop and refine mathematical models for simulation, optimization, and forecasting tasks • Lead the design, development, and optimization of algorithms for data processing, analysis, and modeling • Validate and deploy algorithms in operational environments • Drive innovation by researching and applying advanced techniques in data science, artificial intelligence, and mathematics • Utilize advanced programming skills in Python, R, MATLAB, or similar languages • Leverage data visualization tools to create compelling insights • Serve as a primary point of contact for clients, providing regular updates and ensuring satisfaction with deliverables • Prepare and deliver high-quality technical reports, presentations, and documentation • Facilitate cross-functional collaboration with multidisciplinary teams • Lead research initiatives to explore emerging data science techniques and technologies