Coursera
Consumer · Online learning
- HQ
- Mountain View, US
- Founded
- 2012
- Website
- coursera.org
Consumer · Online learning
Coursera currently has 1 active AI-related role in our index. The most common open titles are: Learning Designer - Technical/AI. Most positions are in Product.
Coursera's active AI hiring is concentrated in: data (100%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Coursera is hiring AI talent in: India (1 role).
Job postings at Coursera most frequently reference: synthetic data, agent orchestration, evals.
In the past 30 days, Coursera has posted 2 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Data Scientist II Data Scientist II at Coursera, focusing on data-driven decision making and data-powered products for content partners and personalized learning. The role involves creating dashboards, analyzing A/B tests, and translating insights into actionable recommendations to improve learner experiences and support business outcomes. Experience with BI tools, SQL, and scripting languages is required, with a preference for those leveraging AI tools to enhance analytical workflows. | — | 5 |
| Senior Data Scientist Senior Data Scientist at Coursera focused on data-driven decision making and data-powered products in the education sector. The role involves product experimentation, causal inference, decision science, and machine learning to shape learner experiences. Key responsibilities include designing and analyzing experiments, building statistical and ML models, and applying psychometric methods to measure learning outcomes. The role leverages GenAI tools to accelerate analysis and aims to personalize learning at scale. | Ship | 5 |
| Data Scientist II Data Scientist II at Coursera, focusing on product analytics to drive data-driven decision making and personalized learning experiences. The role involves analyzing A/B tests, creating dashboards, and translating complex data findings into actionable recommendations for product teams. While the company heavily utilizes AI and the role may use AI tools, the core craft is not building AI models but rather using data to improve the product. | — | 5 |