Staff Data Scientist

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 lifecycle of ML services within Product Analytics, shaping the long-term roadmap for how we leverage advanced analytics and ML to solve high-impact business problems.

What you’ll have

Proven expertise in architecting, developing, and deploying machine learning solutions (e.g., segmentation, forecasting, pricing) that bridge the gap between research and production, ideally in a Product Analytics or platform context.

7+ years of professional experience in data science or applied machine learning, with demonstrated leadership in modular, service-like ML system development.

Deep fluency in Python and SQL; extensive hands-on experience with ML and data engineering libraries (e.g., scikit-learn, pandas, PySpark, MLflow, Airflow).

Track record of designing pragmatic, scalable architectures for ML-powered analytics services that do not require full microservice architectures.

Strong understanding of model lifecycle : from data ingestion to feature engineering, model development, evaluation, deployment, and monitoring.

Expertise in MLOps best practices, including reproducibility, testing, CI / CD, monitoring, and responsible model governance.

Exceptional communication skills, enabling effective collaboration with both technical partners (data scientists and engineers) and business stakeholders (PMs, FP&A, marketing).

Strong business judgment, with the ability to understand product and business drivers relevant to analytics-based ML services.

Experience mentoring data scientists, providing technical guidance, and establishing shared standards in code, modeling, and service design.

Prior experience designing, running, and interpreting A / B experiments and statistical evaluations in a business environment.

Ability and motivation to operate autonomously, prioritize effectively, and influence others as a senior technical leader within Product Analytics.

Deep understanding of the causal inference problem.