Senior Data Scientist - Customer Experience

About Coursera

Coursera and Udemy are now one company, creating one of the world's most comprehensive skills development platforms for the AI era. This strengthens our ability to accelerate AI-powered innovation and shape how the world discovers and builds skills at a pivotal moment of change. Read more about the combined company by visiting our blog .

Coursera was launched in 2012 by Andrew Ng and Daphne Koller with a mission to provide universal access to worldclass learning. Coursera partners with leading university and industry partners to offer a broad catalog of content and credentials, including courses, Specializations, Professional Certificates, and degrees.

Coursera’s platform innovations — including AI-powered personalized guide and features, like Role Play and Course Builder, and role-based solutions like Skills Tracks — enable instructors, partners, and companies to deliver scalable, personalized, and verified learning.

Institutions worldwide rely on Coursera to upskill and reskill their employees, students, and citizens in high-demand fields such as GenAI, data science, technology, and business, while learners globally turn to Coursera to master the skills they need to advance their careers. Coursera is a Delaware public benefit corporation and a B Corp.

Coursera recently combined with Udemy to create one of the world’s most comprehensive skills development platforms.

Why Join Us

At Coursera, we’re looking for inventors, innovators, and lifelong learners ready to shape the future of education. You’ll help build global programs and tools that power online learning for millions turning bold ideas into real impact. People who thrive here are customer-first builders who move fast, simplify ruthlessly, and iterate relentlessly on the metrics that matter.

We’re a globally distributed team that comes together intentionally for collaboration, complex problem-solving, and key milestones — creating opportunities for teams to do their best work together. Our virtual hiring and onboarding experience makes it easy to join us and start making an impact from anywhere. If you’re ready to make a global impact, help scale unique products across Coursera + Udemy, and grow your career, apply below.

Job Overview

As a Senior Data Scientist on the Enterprise CX team, you are a versatile problem-solver with a solid foundation in end-to-end data science methods. You excel in extracting actionable insights from data to drive strategic decisions and enhance revenue growth.

Your expertise lies in conducting deep-dive analyses, diagnosing metric shifts, and applying practical statistical or machine learning methods to solve complex business problems.

You are comfortable self-serving across the data stack when needed, and are eager to work collaboratively with stakeholders to deliver impactful solutions that drive business success.

About this Role

The Senior Data Scientist plays a crucial role in supporting the Customer Success team through deep-dive data analysis, diagnostic investigations, targeted predictive modeling, and applied causal inference.

This position involves working closely with cross-functional teams to drive revenue growth, reduce customer churn, and enhance operational efficiency. Reporting directly to the Manager of Data Science, you will contribute to the development of end-to-end analytical solutions and measure their true business impact.

Cross-functional Collaboration & Communication

Collaborate with cross-functional stakeholders, developing a deep business understanding and supporting synergy across the organization.

Communicate effectively with non-technical stakeholders.

Partner closely with the Customer Success team to provide data-driven insights and support decision-making processes.

End-to-end Analytics

Deep-Dive Analysis: Conduct exploratory data analysis and analytical investigations to diagnose metric shifts and uncover actionable trends in customer behavior.

Applied Modeling: Develop practical predictive models (e.g., churn or upsell forecasting) that directly inform and optimize Customer Success workflows.

Impact Measurement: Apply basic causal inference and experimentation methodologies to evaluate the true business impact of Customer Success initiatives and product changes.

Self-Serve Engineering: Build and modify foundational data pipelines and simple dashboards when needed to unblock analyses, partnering with core Data Engineering and BI teams for scalable infrastructure.

Operational Excellence

Optimize data workflows and contribute to data quality, stepping in to self-serve data extraction and transformation tasks when necessary.

Contribute to the establishment and maintenance of Key Performance Indicators (KPIs) for customer success, leveraging descriptive and diagnostic analytics to drive actionable insights.

Revenue Growth

Utilize deep-dive analysis and pragmatic modeling to assist in monitoring renewals and identify leading indicators of risk and opportunity.

Support ongoing analysis of customer retention, churn, and revenue trends, leveraging both foundational analytics and statistical methods to identify opportunities for growth.

Analytical Support and Proactive Insights

Evaluate business performance to identify the root causes of metric shifts, providing proactive data-driven insights to stakeholders.

Assist in making recommendations to improve business productivity and performance, selecting the right analytical tool—from simple SQL aggregations to statistical modeling—to mitigate risks.

Develop AI/LLM-powered solutions to support CS stakeholders.

Customer Success Collaboration

Work directly with stakeholders in the Customer Success team to create data stories that lead to customer retention and upsell opportunities.

What You’ll Have

Bachelor’s degree or higher in a related field, with a focus on data science, statistics, or a related quantitative discipline.

3-5 years of relevant experience in data science, with a demonstrated ability to conduct deep-dive analyses, diagnose metric shifts, and apply pragmatic modeling techniques to drive business impact.

Proficiency in applied statistics and practical machine learning, with knowledge of causal inference, experimentation (A/B testing), forecasting, and regression.

Advanced proficiency in SQL for complex data extraction and manipulation, alongside a working knowledge of data pipelining tools (e.g., dbt, Airflow) to self-serve when necessary.

Proficiency in programming languages such as Python for data analysis, automation, and modeling.

Working knowledge of Business Intelligence tools (e.g., Tableau, Sigma), with a strong understanding of best practices for dashboarding and data visualization to communicate insights.

Hands-on experience designing and deploying AI/LLM-based solutions.

Strong communication skills, with the ability to convey complex concepts clearly and effectively to stakeholders.

Strong organizational skills, with the ability to manage multiple projects and deadlines effectively.

A tech-curious mindset with a willingness to learn new technologies and methodologies to stay at the forefront of data science innovation.

Compensation

US Zone 3 - 4