Data Analyst

Coursera was launched in 2012 by Andrew Ng and Daphne Koller with a mission to provide universal access to world-class learning. It is now one of the largest online learning platforms in the world, with 183 million registered learners as of June 30, 2025. Coursera partners with over 350 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 enable instructors to deliver scalable, personalized, and verified learning experiences to their learners. Institutions worldwide rely on Coursera to upskill and reskill their employees, citizens, and students in high-demand fields such as GenAI, data science, technology, and business. Coursera is a Delaware public benefit corporation and a B Corp.

Join us in our mission to create a world where anyone, anywhere can transform their life through access to education. We're seeking talented individuals who share our passion and drive to revolutionize the way the world learns.

At Coursera, we are committed to building a globally diverse team and are thrilled to extend employment opportunities to individuals in any country where we have a legal entity. We require candidates to possess eligible working rights and have a compatible timezone overlap with their team to facilitate seamless collaboration. 

Coursera has a commitment to enabling flexibility and workspace choices for employees. Our interviews and onboarding are entirely virtual, providing a smooth and efficient experience for our candidates. As an employee, we enable you to select your main way of working, whether it's from home, one of our offices or hubs, or a co-working space near you.

About the Role
We at Coursera are seeking a highly skilled and collaborative Data Analyst to join our analytics and insights team. The ideal candidate will have 3+ years of experience, with a strong focus on leveraging data and predictive modeling to drive impactful business decisions. This role offers a unique opportunity to work at the intersection of data analysis and data science—building robust dashboards, performing deep-dive analyses, and creating forecasting models to inform strategic initiatives and fuel growth.

Key Responsibilities

• Conduct in-depth data analyses to uncover trends, identify opportunities, and inform key business strategies across Product, CS, and Finance.
• Develop, maintain, and optimize dashboards, reports, and self-serve data products.
• Collaborate with stakeholders to define and measure critical KPIs.
• Build and validate predictive models for churn risk, revenue forecasting, and growth opportunities.
• Use NLP and AI models to analyze unstructured data (e.g., support tickets, sentiment data, executive engagement) and extract actionable themes and signals.
• Drive insights-based storytelling—translating data into clear, impactful recommendations.
• Partner closely with data engineering and product teams to ensure data integrity and enable decision-making.

Qualifications

• Education:
  • Bachelor’s degree in Statistics, Data Science, Computer Science, Economics, or a related quantitative field
• Experience:
  • 3+ years of experience in data analysis, data science, or analytics roles
  • Proven ability to drive insightful analysis and impactful recommendations
  • Experience building predictive models for revenue forecasting, churn risk, or related areas
• Technical Skills: 
  •   Proficiency in SQL for data extraction, data cleaning, and transformation.
  •   Strong programming skills in Python (Pandas, NumPy, Scikit-Learn, etc.) for data analysis and model development.
  •   Experience with data visualization tools (e.g., Tableau, Looker, Power BI) to build clear, actionable dashboards.
  •   Ability to apply statistical methods and A/B testing frameworks to solve business problems.
  •   Familiarity with NLP/AI techniques to extract insights from unstructured data such as support tickets, customer feedback, and sentiment data.
  •   Understanding of data engineering principles to ensure data quality and readiness for analysis.
  •   Comfort working with large datasets and performing complex joins, aggregations, and data modeling.

#LI-SG1