Data Science Manager

JR United Kingdom; City Of London; On-site; GBP 90,000 - 110,000; Full time

Job summary

A leading insurance client is seeking a Data Science Manager based in London. The role involves enhancing data science capabilities, leading projects like Algorithmic Risk Ranking, and collaborating with various teams to deliver advanced analytical solutions. Ideal candidates should have strong leadership and data science skills, along with experience in the insurance market.

Qualifications

• Proven leadership experience in a data role.
• Expertise in data science methods and tools.
• Advanced programming skills, ideally with Python.

Responsibilities

• Lead the Data Science function and deliver ML-driven underwriting solutions.
• Enhance pricing models with advanced statistical methods.
• Manage a team of Data Scientists aligning their work with business goals.

Skills

Leadership experience

Data science methods and tools

Python programming

Statistical skills

Data manipulation

Analytical skills

Stakeholder engagement

Collaboration with IT specialists

Data-driven use cases delivery

Azure technologies knowledge

Tools

Azure Data Factory

SQL

Azure Synapse

Power BI

Job description

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Data Science Manager, London (City of London)

Client: Heat Recruitment

Location: London (City of London), United Kingdom

Job Category: Other

EU work permit required: Yes

Job Views:

3

Posted:

31.05.2025

Expiry Date:

15.07.2025

Job Description:

Job title: Data Science Manager

Location: Hybrid – 2/3 days per week in Central London

Salary: Up to 110k depending on experience

Our insurance client is looking for a Data Science Manager to join their growing data team to help enhance the data science capability of the business. You will take ownership of various projects such as Algorithmic Risk Ranking, Digital-Follow, Machine Learning Profitability Analysis and Pricing. You will also work closely with the Data Analytics Manager to define the strategic data requirements for delivery of data consumption use cases.

Skills required:

• Proven leadership experience within a data role
• Expertise in data science methods and tools
• Advanced programming skills, ideally with Python
• Extensive experience in Data Science or Actuarial roles, ideally in the Lloyd's or broader insurance market
• Strong statistical, data manipulation, and analytical skills
• Excellent stakeholder engagement across technical teams and senior leadership
• Experience collaborating with Data Engineers, Architects, and IT specialists
• Experience delivering data-driven use cases from concept to execution
• Proficiency in key IT tools; experience with Azure technologies (Data Factory, SQL, Synapse, Power BI) is a plus

Summary of role:

• Lead the Data Science function, delivering solutions that support underwriting decisions, including ML-driven Risk Ranking and profitability analysis.
• Develop the Digital-Follow channel in partnership with the Digital Trading team, using broker data to assess portfolio performance and build optimised rule sets aligned with risk appetite.
• Enhance pricing models by collaborating with actuaries to apply advanced statistical methods and monitor model performance.
• Use AI to automate and optimise data pipelines and analytics processes, such as Cause of Loss classification.
• Leverage external data sources to uncover insights through cleansing, analysis, and visualisation in collaboration with Data Analysts and Actuaries.
• Promote analytical capabilities across the business, working closely with the Head of Data Analytics and Portfolio Underwriting.
• Participate in governance groups (e.g., Design Authority, Pricing Working Group) to shape data science solutions and maintain approval oversight for model changes.
• Manage and prioritise a team of Data Scientists, aligning their work with strategic business goals.
• Define strategic data requirements alongside the Data Analytics Manager, translating business needs into technical specifications.
• Liaise with stakeholders (technical and non-technical) to document and prioritise data requirements.
• Collaborate with IT and Data Engineers to design and implement scalable data solutions.
• Ensure data quality and governance by establishing and maintaining best practices and reporting standards.