Data Scientist

About Insurance Insider 
Insurance Insider is the leading provider of insights and analysis for the world’s top insurers, distributors, service providers, and investors. 

Since 1996, we’ve been helping clients uncover new business opportunities and protect against risks through our exclusive news, deep analysis and actionable insights on the insurance market. Our coverage extends across the London market, global (re)insurance market, insurance-linked securities market and US property and casualty market. 

About the Role 

We're looking for a Data Scientist with growing data science skills to join our data team. 

Much of the role is about structuring and formatting data - turning usage data, article content and other inputs into clean, ready-to-use formats that power personalisation, content recommendations and other product features. So the traditional data engineering work of building and maintaining pipelines will form the base, but the skills to derive value from that data are what's key for this role, along with the ability to use AI to do that quickly. 

This may suit someone 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.
We'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. 

We’re looking for a self-starter, able to work by themselves and not need to be told what to do. Their mindset is as important as their technical skills - they will be comfortable with experimentation and not necessarily following a strict process. They will have a passion to build products which can create business value and will be working as part of a small data team of 3. 

Key Responsibilities 

Data Structuring & Preparation 

• 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. 
  
  

Personalisation & Content Intelligence 

• 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 

• 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, and 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 

• 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. 
  
  

AI-Assisted Development & Collaboration 

• 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. 
  
  

Required Experience 

• 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

• 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. 
  
  

Preferred Qualifications 

• 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. We care more about the quality of your work than where you studied. 

• Experience with orchestration tools such as Airflow, Dagster or Databricks Workflows. 

• They may have some data analytics experience - querying and storing data, data preparation, data pipelines and visualisation.   

What We Offer 

• A role with real scope to shape how the business understands its customers, with room to grow into a broader remit over time 
• Real trust and autonomy – we back people who take ownership and run with initiative 
• Flexibility with true hybrid working – expected to be in the office 1 day a week 
• Competitive compensation and benefits package. 
• 25 holiday days per year, plus your birthday off 
• A collaborative and mission-driven culture 
• Opportunities for professional growth and development