Overview
We are seeking a Senior Data Scientist / ML Engineer to contribute to a Databricks platform initiative within an international events company. The successful candidate will engage in a hands-on role focusing on MLOps infrastructure, the development of a recommendation engine, and identity resolution for customer data. The contractor will collaborate with an internal team, set MLOps standards, and ensure knowledge transfer for ongoing team independence post-engagement.
Responsibilities
- Design and implement an end-to-end MLOps framework including model tracking, versioning, and monitoring.
- Develop and execute a recommendation system approach for enhancing exhibitor and session matching based on attendee preferences.
- Construct a tiered matching pipeline utilizing both deterministic and fuzzy methodologies for creating a unified customer view.
- Establish ML standards and conventions for the client's internal team to ensure ongoing independent operations.
- Document MLOps reference architecture, feature specifications, and model runbooks.
- Conduct knowledge transfer sessions to equip the internal team with the necessary skills for continued work without external support.
Requirements
- Minimum of 5 years of experience in applied machine learning or data science with a focus on production delivery.
- Demonstrated end-to-end ownership of at least one machine learning project, from design to production.
- Production-level experience with MLOps on Databricks or similar platforms, including MLflow and model serving.
- Proficiency in building recommendation systems with an emphasis on embedding-based retrieval and vector search.
- Experience with entity resolution and fuzzy matching techniques, including Jaro-Winkler and Levenshtein algorithms.
- Strong skills in Python (including PySpark, pandas) and SQL.
- Familiarity with Delta Lake, Unity Catalog, and Lakeflow Jobs.
- Experience with Git-based CI/CD processes for machine learning models.