At a Glance
- Tasks: Design and implement cutting-edge ML solutions for an international events company.
- Company: Dynamic events company leveraging innovative data science on a hybrid work model.
- Benefits: Competitive day rate, flexible working environment, and opportunities for professional growth.
- Other info: Collaborative team culture with a focus on knowledge sharing and independence.
- Why this job: Join a hands-on role to shape the future of data-driven events and make a real impact.
- Qualifications: 5+ years in applied ML/data science with strong production delivery experience.
The predicted salary is between 70000 - 90000 £ per year.
Location: London (hybrid)
Day rate: Up to £545 per day
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 ingest
- 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
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 production
- 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 scaling)
- Strong hands-on Python (PySpark, pandas, ML libraries) and SQL
- Working experience with Delta Lake, Unity Catalog, and Lakeflow
- 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 work
Data Scientist in London employer: Primus Connect
Join a dynamic and innovative company that prioritises collaboration and employee growth, making it an excellent employer for a Data Engineering Manager. With a strong focus on mentorship, you will have the opportunity to lead a growing team while working in a vibrant office environment in central London/Guildford, complete with a free onsite restaurant and gym. This role not only allows you to shape impactful data platforms but also fosters a culture of rapid delivery and teamwork, ensuring your contributions are valued and recognised.
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We think this is how you could land Data Scientist in London
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We think you need these skills to ace Data Scientist in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Primus Connect, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Primus Connect. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Primus Connect
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
