Udemy is seeking to transform the future of learning by building an AI-powered reskilling platform that helps individuals and teams grow through personalized, practical, and impactful learning solutions.
Requirements
- 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.
- Deep understanding of the causal inference problem.
Responsibilities
- 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.
- Design, run, and interpret A/B experiments and statistical evaluations in a business environment.
Other
- This is an in-office position, requiring three days a week in the office (Tuesday, Wednesday, Thursday) and flexibility on Mondays and Fridays.
- 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.
- Ability and motivation to operate autonomously, prioritize effectively, and influence others as a senior technical leader within Product Analytics.
