Staff Data Scientist

Join Udemy. Help define the future of learning.

Udemy is an AI-powered reskilling platform built to help people and teams grow. It’s personalized, practical, and focused on real-world impact.

Our mission is simple: to transform lives through learning. Your work helps people around the world build skills they can use, whether they’re picking up something new or leveling up to stay ahead.

Over 80 million learners and 17,000 businesses already learn with Udemy. If you’re excited by change, energized by learning, and ready to have a real impact, you’ll feel right at home. 

Learn more about us on our company page.

Where we work

Udemy is a global company headquartered in San Francisco, with additional U.S. offices in Denver and Austin, and international hubs in Australia, India, Ireland, Mexico, and Türkiye. This is an in-office position, requiring three days a week in the office (Tuesday, Wednesday, Thursday) and flexibility on Mondays and Fridays.

About your skills

 

• Building: You are a hands-on builder, designing and constructing intelligent, reliable machine learning systems and analytics pipelines from the ground up. Drawing on deep expertise in statistical methods, causal inference, and ML best practices, you create modular, reproducible solutions for segmentation, forecasting, inference, and experimentation. You ensure that every component—from feature engineering to deployment and monitoring—is built with scalability, code quality, and robust performance in mind, transforming ambiguous requirements into production-ready analytics services that deliver measurable business value.

 

• Influencing: You foster relationships across engineering, product, and analytics teams, utilizing your credibility and communication skills to advocate for best practices and align requirements. You ensure technical designs satisfy both immediate project needs and broader strategic priorities, constructively challenging assumptions when necessary to deliver optimal impact.

 

• Technical Decision Making: You apply rigorous critical thinking and a structured approach to technical problem solving, drawing on your deep expertise in ML, MLOps, and software architecture. 

 

• Coaching and Mentorship: You share your knowledge generously, actively listening to understand team needs and offering targeted guidance on advanced machine learning practices  and code quality. Your mentorship elevates team capability, fosters continuous learning, and ensures high standards in modeling.

 

 

About this role

 

 

In the Data organization at Udemy, we’re passionate about transforming the future of education using data. We’re looking for fun, collaborative, self-motivated data scientists with an insatiable sense of curiosity and a knack for asking the right questions to join our Product Analytics team. 

You will design, build, and maintain production data systems that power Udemy’s most impactful machine learning products. You’ll collaborate closely with data scientists, product managers, engineers, and marketers to deliver high-quality, robust data-driven solutions. This role is ideal for someone who combines strong software engineering and data science expertise with excellent communication skills and a passion for turning complex challenges into real business impact.

 

What you’ll be doing

• Architect and develop intelligent, scalable, and maintainable solutions for key analytics services, including user segmentation, forecasting, and dynamic pricing, used across Udemy’s product and business teams.

• Collaborate cross-functionally with product managers, engineers, and analysts to define requirements for ML-driven systems, understand business goals, and translate ambiguous needs into clear technical designs.

• Lead the development and scaling of modular, reusable ML components (data pipelines, workflows, and frameworks for evaluation, monitoring and retraining) to power robust, trustworthy services and ensure ongoing reliability of deployed ML systems.

• Play a central role in raising the standard for code quality, data hygiene, reproducibility, and infrastructure with respect to analytics-centric machine learning systems.

• Mentor and upskill other team members on topics ranging from advanced machine learning concepts to software engineering best practices when building out analytics infrastructure.

• Act as the subject matter expert on the life