Data Scientist

Our client is the leading source of actionable intelligence for the global insurance, reinsurance and ILS markets. They are building a new AI-powered data product and need someone to help them turning their growing pool of data into the structured, ready-to-use formats that power search, personalisation and new features. They need a Data Scientist with a software or data engineering background who has moved into data science over the past few years and wants to keep developing in that direction. Or a generalist who's happy moving between engineering and data science work rather than a deep specialist in either. They'll soon be bringing in a lot of new data and new data types from external sources, which will make this data structuring and manipulation work even more central to the role. Your day-to-day impact: ✔ Turn raw, messy or unstructured data - article text, usage data, and new data arriving from external sources - into clean, ready-to-use formats for downstream products. ✔ Apply light ML/NLP techniques (e.g. entity extraction, classification, tagging) to structure unstructured text. ✔ Adapt existing pipelines to ingest and normalise new data types as they come online. ✔ Bring in new datasets - scraped or paid-for/third-party - and merge them with our existing data to create new, value-add content sets. ✔ Build personalisation logic that blends usage data with content suggestions, helping surface the right content to the right customer. ✔ Work with product and editorial to define the signals and rules behind personalised recommendations and content creation. Data Science tasks: ✔ Use statistical and ML methods (e.g. classification, clustering, embeddings) to analyse usage and content data, surfacing patterns and insights that inform personalisation and product decisions. ✔ Use LLMs to extract structure - keywords, entities, topics - from unstructured article text, and check the accuracy and quality of that output. ✔ Prototype and test simple models or scoring logic (e.g. for recommendations or content matching), working with product to validate they add value before scaling. ✔ Try different approaches to a data or personalisation problem, evaluate what works, and explain the trade-offs in plain terms to non-technical stakeholders. Data Engineering tasks: ✔ Maintain and extend our Databricks/Python pipelines - ingestion, transformation, scheduling and monitoring. ✔ Identify new data sources relevant to the product and help scope their integration. ✔ Use AI coding tools (e.g. GitHub Copilot, Claude, Cursor) to build and iterate on data pipelines and structuring work quickly. ✔ Work across product, editorial and commercial teams to understand data needs and communicate findings clearly to non-technical audiences. ✔ Monitor data quality in production, flagging issues and improving tooling and documentation. Your profile looks like that: ✔ 3–5 years' hands-on experience in data engineering, with strong Python and SQL. ✔ Experience building and maintaining data pipelines, ideally with Databricks, Spark or a comparable cloud data platform. ✔ Some exposure to - or a strong interest in developing - data science/ML techniques, e.g. classification, clustering, NER, embeddings. ✔ Experience turning raw or messy data into structured, ready-to-use formats for downstream use. ✔ Comfortable working independently and using AI coding tools to move quickly. You are a great fit if you have: ✔ Experience with personalisation, recommendation systems, or blending usage/behavioural data with content. ✔ Exposure to using LLMs to extract structure (keywords, entities, topics) from unstructured text. ✔ A degree in Computer Science, Data Science, Engineering or a related field - or equivalent experience. ✔ Experience with orchestration tools such as Airflow, Dagster or Databricks Workflows. They offer, not just a nice team and environment, but: ✔︎ Flexibility with true hybrid working - expected to be in the office 1-2 days a week. ✔︎ 25 holiday days per year, plus your birthday off. ✔︎ Opportunities for professional growth and development. ✔︎ Competitive compensation and benefits package. A re you still here? Great! Let’s discuss further at [email protected] (Recruitment License № 2709/ 17.01.2019)