Primus Connect
Data Scientist
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Data Scientist / ML Engineer | Databricks, MLOps & Recommendation Systems
Location: London (hybrid)
Contract length: 4 months Initial
Day rate: Up to £545 per day
Engagement: Outside IR35 contract
About the Role
We're recruiting a senior Data Scientist / ML Engineer for a Databricks platform buildout with an international events company. This is a hands-on delivery role covering three interconnected workstreams: MLOps foundations, a recommendation engine for exhibitors and sessions, and identity resolution across sparse customer registration data.
You'll be responsible for both defining the ML approach and building it end-to-end, setting the standards (MLOps patterns, feature engineering conventions, model lifecycle, matching logic) that the internal team will operate and extend after you hand over.
What You'll Be Doing
- Designing and implementing an MLOps framework end-to-end: model tracking, versioning, promotion, serving, monitoring, and feedback ingestion
- Designing, experimenting with, and implementing a recommendation approach for exhibitors and sessions based on attendee preferences
- Building a tiered matching pipeline (deterministic + fuzzy) for a single customer view, including feature engineering and a human-in-the-loop feedback loop
- Setting ML standards and conventions the client team can follow independently
- Owning documentation: MLOps reference architecture, feature specifications, matching rules, model runbooks
- Running knowledge transfer sessions so the internal team can operate, extend, and iterate without external support
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What We're Looking For
- 5+ years in applied ML/data science with strong hands-on production delivery
- Proven end-to-end ownership of at least one ML delivery: design through productionisation
- Production experience with MLOps on Databricks (or a directly comparable platform) — MLflow, Feature Store, model serving, monitoring, drift detection
- Production experience building recommendation systems or embedding-based retrieval, ideally with vector search
- Production experience with entity resolution / fuzzy matching pipelines (Jaro-Winkler, Levenshtein, Soundex or equivalent, plus blocking strategies for scale)
- Strong hands-on Python (PySpark, pandas, ML libraries) and SQL
- Working experience with Delta Lake, Unity Catalog, and Lakeflow
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Jobs
- Experience with Git-based CI/CD for ML pipelines (Databricks Asset Bundles, GitHub Actions, or equivalent)
- Prior customer-facing or consulting experience, comfortable running knowledge transfer and leaving a team self-sufficient
Nice to Have
- Experience building lightweight UIs on Databricks Apps for human-in-the-loop workflows
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