About this role.
Coursera, now combined with Udemy, is seeking a Senior Data Scientist for the Enterprise CX team. This role involves deep-dive data analysis, diagnostic investigations, predictive modeling, and causal inference to support the Customer Success team. The position requires cross-functional collaboration and effective communication with non-technical stakeholders to drive revenue growth and reduce customer churn. Reporting to the Manager of Data Science, you will develop end-to-end analytical solutions and measure their business impact. It's a fast-paced, globally distributed environment focused on AI-powered education innovation.
Role DNA
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Job Complexity
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5/5Salary analysis
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Core skills
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Cover letter sample
Dear Hiring Manager,
I am excited to apply for the Senior Data Scientist - Customer Experience position at Coursera. With a strong background in end-to-end data science, including predictive modeling, causal inference, and deep-dive analytics, I have successfully driven revenue growth and reduced churn in previous roles. I am particularly drawn to Coursera's mission of universal access to world-class learning and the opportunity to work on the combined Coursera + Udemy platform. My experience collaborating with customer success teams and translating complex data into actionable insights makes me a great fit for this role.
I look forward to contributing to your team and helping shape the future of education through data-driven decisions.
Sincerely, [Your Name]
Sample interview questions
In my previous role, I noticed a 15% drop in weekly active users. I initiated a deep-dive analysis by segmenting users by cohort and behavior, and used SQL to query logs. I found that a recent product update caused a lag for users on older devices. I presented this finding to product and engineering teams, who rolled back the update, recovering the metric within a week. This experience taught me the importance of quick, systematic diagnosis and clear communication.
I would start by defining churn clearly and gathering historical data on customer usage, engagement, support tickets, and contract renewal patterns. Features might include login frequency, feature adoption, NPS scores, support interactions, and account demographic data. I would explore tree-based models like XGBoost for interpretability and run feature importance analysis. I'd also implement a monitoring plan to track model performance over time and collaborate with the customer success team to act on predictions.
I would focus on the business impact rather than the technical details. For example, I might say, 'Our analysis shows that customers who use the reporting feature at least three times a month are 40% less likely to churn. By encouraging adoption of this feature, we could reduce churn by X%.' I would use simple visuals like charts and graphs, and avoid jargon. I'd also invite questions and provide a short summary in writing for reference.
I would set up a randomized controlled trial where new customers are split into control and treatment groups. The treatment group receives the new onboarding, while the control receives the standard experience. I'd measure retention rates at 30, 60, and 90 days, ensuring the sample size is adequate for statistical significance. I'd also consider potential confounders and use techniques like stratified random sampling. Finally, I'd analyze the results with t-tests or regression models, and include a guardrail on metrics like support load.
At a previous company, we wanted to know if sending a discount email increased customer spend. Since we only sent to at-risk customers, selection bias was an issue. I used a propensity score matching approach to create a comparable control group. This allowed us to estimate the causal effect of the email, which showed a modest but significant lift in spend. The challenge was ensuring that all confounders were measured, but sensitivity analysis helped validate the results.
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.
What You’ll Be Doing:
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
$132,000 – $166,000 USD
The range(s) listed above is the expected annual base salary for this role, subject to change.
Salary is just one component of Coursera’s total rewards package. All regular employees are also eligible for a bonus program and equity in the form of RSU’s.
A number of factors are taken into account when determining pay, which includes: job level, location, training/education, business need, skill set and internal equity.
Current Zone Locations:
- Zone 3 – CA (outside of SF Bay Area), CO, CT, DC, GA, IL, MA, MD, NY/NJ (outside of NYC Metro), OR, RI, TX, VA, WA (outside of Seattle Metro)
For more information about how Coursera collects and uses your personal information, please see our Global Applicant Privacy Notice.
To protect against recruitment fraud, Coursera + Udemy recruiters only communicate via official coursera.org/udemy.com email addresses and never through personal accounts. We do not accept resumes via email or social media; please submit all applications directly through our careers page.
If you encounter suspicious recruitment activity, please report it via our Fraudulent Activity Submission Form.
Coursera is an Equal Opportunity Employer committed to building a welcoming and inclusive workplace. We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request at recruiting@coursera.org.
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