Design, development, and maintenance of end-to-end ML pipelines for training, evaluation, and deployment across batch and real-time use cases
Productionization of ML models via APIs or batch inference, including telemetry, A/B testing, drift detection, and automated monitoring
Build reliable data preparation and feature engineering components
Optimize model training and inference performance and cost, including hardware selection, caching, vectorization, quantization, and scalable endpoints
Establish and maintain CI/CD workflows for ML systems; contribute to platform standards, documentation, and operational runbooks

Bachelor's or Master's degree in Data Science, Computer Science, Engineering, Mathematics, or a related field
Several years of experience building production-ready ML systems with Python and ML frameworks (pandas, numpy, scikit-learn, PyTorch, TensorFlow) as well as containerization and CI/CD (Git, Docker, orchestration/workflows)
Proficiency in SQL and data modeling, with experience performing exploratory data analysis (EDA) to identify patterns and dependencies
Experience with vector indices, embedding models, LLM agent patterns, ingestion pipelines, and model serving frameworks (e.g., MLflow, Databricks)
Experience with unified data/AI platforms such as Databricks, including Unity Catalog, governance concepts, A/B testing, causal inference, and experimentation platforms
Languages: English – fluent
Nice to have: relevant cloud or data/AI certifications
Familiarity with writing and debugging LLM tools in OOP-style Python, with a focus on generative AI (prompting, RAG, vector search) and MLOps

Senior Data Scientist – all genders

Key skills