INDUSTRY
OVERVIEW
The insurance industry is undergoing a data analytics revolution. As businesses increasingly rely on data-driven insights to drive decision-making, the demand for data analytics professionals who can provide risk management solutions within the insurance sector is surging.
In recent years, the realm of data and analytics has gained paramount importance, resulting in the emergence of specialized roles such as credit analysts, data scientists, data engineers, and analytics developers. One of the key challenges in Insurance Data Recruitment is identifying individuals with the right skill set to aid their company's risk management services. These positions necessitate expertise in programming languages, data modelling, statistical analysis, and knowledge of advanced data analytics techniques. All whilst working within the organisation's risk management framework, which varies from company to company.
Currently, there exists a notable scarcity of skilled data analytics professionals, rendering it challenging for insurance companies to identify the right talent to fill crucial roles. The demand for skilled professionals in Insurance Data Recruitment is expected to grow in the coming years as the insurance industry continues to evolve. Consequently, a competitive landscape has started to emerge, characterized by escalating salaries and benefits as organizations vie for qualified candidates.
Attracting and retaining top talent through insurance data recruitment insurance companies must cultivate a supportive and innovative workplace culture that nurtures growth, learning, and collaboration. Insurance Data Recruitment is a strategic imperative for these companies. By investing in the recruitment, training and development of their workforce, insurers can secure the retention of skilled professionals and maintain a competitive edge in the data analytics-driven insurance landscape. Contact one of our insurance data recruitment experts today to stay ahead in this evolving industry.
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CORE SKILLS
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Data Analysis: Data analysts in insurance need a strong grasp of data analysis techniques. This involves the ability to clean, preprocess, and transform raw data into a usable format. Analysts must also be skilled in exploratory data analysis to uncover patterns, correlations, and anomalies within insurance datasets. This skill is crucial for identifying key insights that can inform business decisions and risk assessment.
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Statistical Analysis: Statistical analysis is at the core of insurance data analytics. Analysts must be proficient in statistical methods to assess risk, model claim frequencies and severities, and conduct actuarial analyses. This skill is essential for pricing insurance products accurately and for making underwriting decisions.
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Data Visualization: Data analysts need to translate complex data findings into easily understandable visualizations. Proficiency in data visualization tools like Tableau or Power BI is essential. Clear and compelling visualizations help stakeholders, including underwriters and executives, comprehend data-driven insights and make informed decisions.
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Programming Languages: Data analysts often work with programming languages like Python or R to manipulate and analyze data efficiently. These languages enable analysts to write custom scripts and algorithms for data transformation, statistical modeling, and machine learning applications. Python, in particular, is widely used in the insurance industry for data analytics.
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Machine Learning: Machine learning is increasingly important in insurance data analytics. Analysts must have a foundational understanding of machine learning algorithms and techniques. These skills are used for tasks such as predicting insurance claims, identifying fraud, and optimizing customer segmentation.
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Insurance Domain Knowledge: Understanding the insurance industry's nuances, including various insurance products (e.g., life, health, property, casualty), policy structures, and regulatory requirements, is critical. Domain knowledge allows data analysts to contextualize data and tailor their analyses to address specific insurance-related challenges.
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Business Acumen: Data analysts should have a strong sense of the insurance business. They need to align their data analytics efforts with the company's strategic goals and objectives. This entails understanding the insurance market, competitive landscape, and customer needs.
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Ethical Data Handling: Data privacy and ethical considerations are paramount when working with sensitive customer data in the insurance industry. Analysts must ensure compliance with data protection regulations and industry standards to maintain customer trust and legal integrity.
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Team Collaboration: Effective communication and collaboration skills are essential. Data analysts often work in cross-functional teams alongside underwriters, actuaries, and IT professionals. The ability to communicate data findings clearly and collaborate on solutions is crucial for success.
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Adaptability: The insurance industry, like many others, is continuously evolving. Data analysts should be adaptable and open to learning new data tools, technologies, and methodologies. Staying updated with industry trends and emerging data analytics techniques is important.
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HIRING DATA SCIENTISTS?Â
A data scientist's role in the insurance industry is multifaceted and essential for leveraging data to make informed decisions, optimize processes, and drive business growth.
To hire and retain the best Data Scientists, organisations should:
- Provide competitive salary and benefits packages
- Professional Growth Opportunities
- Innovative Work Environment
- Build a strong employer brand
- Establish strategic partnerships with universities and research institutions.
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UK Hiring Market:
In the competitive landscape of the UK insurance industry, the demand for skilled data scientists is steadily increasing. The UK's insurance sector is renowned for its innovative approach and reliance on data analytics to refine risk assessment and customer engagement. To attract top data science talent, insurance companies in the UK should implement strategic recruitment practices and emphasize their commitment to technological advancements and data-driven decision-making.
US Hiring Market:
In the United States, there is a surging demand for data scientists in the insurance sector. Insurance companies, insurtech firms, and startups are heavily investing in data analytics to optimize underwriting processes, enhance customer experiences, and develop predictive models for risk management. To compete for data science talent in the US, insurance organizations must offer attractive compensation packages and highlight their involvement in cutting-edge data projects.
EU Hiring Market:
The European insurance market exhibits varying levels of demand for data scientists across different countries. Nations with a strong insurance presence, such as Germany, France, and Sweden, are experiencing a substantial need for data scientists proficient in insurance data analytics and regulatory compliance. Insurance companies in these regions should target local talent pools and emphasize their industry expertise to attract skilled data scientists.
Speak to one of our insurance data recruitment experts today to find a data scientist to suit all your needs!
HIRING RISK ANALYSTS?
HIRING DATA ENGINEERS?
HIRING CREDIT ANALYSTS?
In the insurance industry, credit analysts play a pivotal role in assessing and managing the financial risks associated with policyholders and potential clients.
To hire and retain the best Credit Analysts, organisations should:
- Provide competitive salary and benefits packages
- Professional Growth Opportunities
- Innovative Work Environment
- Build a strong employer brand
- Establish strategic partnerships with universities and research institutions.
HIRING BUSINESS INTELLIGENCE ANALYSTS?
In the insurance industry, business intelligence (BI) analysts serve as the data maestros, orchestrating the management and utilization of vast datasets essential for informed decision-making. Their role is multifaceted, encompassing various critical responsibilities that collectively enhance the overall efficiency and effectiveness of insurance operations.
To hire and retain the best BI Analysts, organisations should:
- Provide competitive salary and benefits packages
- Professional Growth Opportunities
- Innovative Work Environment
- Build a strong employer brand
- Establish strategic partnerships with universities and research institutions.
UK Hiring Market:
In the competitive landscape of the UK insurance industry, the demand for skilled BI analysts is on the rise. The UK's insurance sector relies heavily on data-driven insights for risk assessment, customer segmentation, and operational optimization. To attract top BI analyst talent, insurance companies in the UK should implement strategic recruitment practices and emphasize their commitment to leveraging data for competitive advantage.
US Hiring Market:
In the United States, there is a growing demand for BI analysts in the insurance sector. Insurance companies, including carriers and brokers, are increasingly adopting BI solutions to gain insights into customer behaviour, claims processing efficiency, and market trends. To compete for BI analyst talent in the US, insurance organizations should offer attractive compensation packages and showcase their involvement in cutting-edge BI projects.
EU Hiring Market:
The European insurance market also presents opportunities for BI analysts, particularly in countries with strong insurance industry presence such as Germany, France, and Sweden. These regions require BI analysts proficient in data analytics and visualization to support data-driven decision-making and enhance operational efficiency. Insurance companies in these areas should focus on local talent pools and emphasize their industry expertise to attract BI analysts who can unlock valuable insights from data to drive business growth and competitiveness.
Need help to hire the right BI Analyst? Contact us today and choose from our diverse talent pool.
JOBS
LATEST Insurance Data
JOBS
Senior Data Scientist
London
£60000 - £75000
+ Risk Analytics
PermanentLondon
To Apply for this Job Click Here
Senior Data Scientist
Salary
£60,000-£75,000 + bonus
Location + work pattern
London or Leicester | Hybrid working (1 day per week in the office)
THE COMPANY
- A well-established UK general insurance provider with a strong reputation for advanced analytics and data-driven decision making.
- The business operates at scale across multiple personal lines products and continues to invest significantly in modern data platforms and machine learning.
- Data science sits at the core of how the organisation prices risk and drives commercial strategy.
THE ROLE
This is a senior individual contributor role within an innovative pricing function, focused on developing and evolving advanced predictive models. You will work on complex, high-impact modelling problems and help shape the next generation of pricing approaches.
Specifically, you can expect to be involved in:
- Building and enhancing pricing and risk models using large, complex and messy datasets.
- Developing predictive and residual models to better understand expected versus actual outcomes.
- Exploring novel data sources, including geospatial and alternative datasets, to improve customer selection and pricing accuracy.
- Supporting the progression of existing models from early versions into more mature, production-ready solutions.
- Mentoring junior team members and contributing to best practice across the data science community.
YOUR SKILLS AND EXPERIENCE
- Strong experience in predictive modelling using Python and SQL.
- Hands-on experience with modern machine learning techniques such as gradient boosting methods.
- Proven ability to work with large-scale, complex datasets and translate ambiguity into clear analytical approaches.
- Experience deploying models into production environments within a cloud-based platform.
- Ability to communicate technical concepts clearly and support less experienced colleagues.
THE BENEFITS
- Performance-related bonus.
- Car allowance.
- Private medical insurance and health & wellbeing support.
- Generous holiday allowance with the option to buy or sell additional days.
- Pension contribution with employer matching.
- Life assurance, income protection and additional optional benefits.
THE PROCESS
- Initial 30-minute interview focused on experience and approach.
- Technical case study with presentation and discussion.
- Competency-based interview covering leadership and delivery scenarios.
- Final-stage interview focused on team fit and senior stakeholder engagement.
HOW TO APPLY
Please register your interest via the apply link on this page.
To Apply for this Job Click Here
Pricing Analytics Manager
City of London
£60000 - £75000
+ Risk Analytics
PermanentCity of London, London
To Apply for this Job Click Here
Pricing Analytics Manager
Hybrid – 1x day per week (London, Leicester or Bexhill)
£60,000 – £75,000 + car allowance.
This is a high impact pricing analytics role sitting at the centre of portfolio performance and underwriting strategy. You will influence how risk is assessed, priced and managed across a large, complex insurance portfolio, with clear visibility and ownership over commercial outcomes.
The Company
They are a digitally led financial services business with a strong data and analytics capability at the core of decision making. With millions of customers and continued investment in pricing and analytics, they place real emphasis on curiosity, collaboration and constructive challenge. The culture is values driven, focused on delivering fair customer outcomes while achieving sustainable growth.
The Role
You will join a specialist pricing analytics team focused on portfolio oversight and risk performance. Acting as a key link between modelling, pricing and underwriting, you will turn complex performance data into clear, actionable insight.
- Analyse portfolio risk using loss ratios and performance KPIs across modelled segments
- Identify areas of over and under performance and translate findings into portfolio actions
- Provide data driven insight to pricing and modelling teams to inform rate and model changes
- Lead risk selection initiatives and cross functional projects that deliver commercial value
- Communicate insights clearly to senior and non technical stakeholders
- Support underwriting governance and ensure customer outcomes remain fair and appropriate
Your Skills and Experience
- Strong commercial experience in pricing, underwriting or portfolio analytics within insurance or financial services
- High level of confidence working with numerical and statistical data
- Experience using data manipulation or statistical tools, with appetite to develop advanced capability
- Proven ability to critically evaluate information and challenge assumptions
- Clear and confident communicator, comfortable influencing stakeholders
- Proactive mindset with a track record of driving change through insight
What They Offer
- Competitive salary plus £5k car allowance
- Annual performance related bonus
- Flexible hybrid working approach
- Private medical insurance and comprehensive wellbeing support
- Generous pension contributions and life assurance
- 27 days annual leave plus bank holidays, with buy and sell options
- Ongoing training, development and genuine career progression in pricing and analytics
How to Apply
Apply now to learn more about this opportunity and how you can shape portfolio performance through data driven insight.
To Apply for this Job Click Here
Senior Data Scientist
London
£100000 - £130000
+ Risk Analytics
PermanentLondon
To Apply for this Job Click Here
Senior Data Scientist
London
£100,000 to £130,000
This is an opportunity to step into a highly influential data science role within a business where analytics sits at the heart of commercial decision making. You will work on complex, high-impact pricing problems, have clear ownership of your work, and see your models directly shape business outcomes.
The Company
They are a technology-led organisation operating in the insurance space, known for their strong investment in data, analytics and engineering. The business is well funded, ambitious, and sets high standards for analytical thinking and technical quality. Data scientists are viewed as strategic partners, not service providers, and are encouraged to challenge, innovate and drive measurable impact.
The Role
- Deliver data science within a pricing-focused environment, influencing rates, tariffs and overall market competitiveness
- Design, build and refine predictive models to better understand risk, customer behaviour and price sensitivity
- Identify and integrate new data sources to improve pricing accuracy and decision making
- Use simulation and modelling techniques to estimate outcomes such as claim frequency and customer responses to price changes
- Translate complex analysis into clear, commercially focused recommendations for senior stakeholders
Your Skills & Experience
- A strong academic background and Masters degree in a STEM discipline such as mathematics or physics
- Advanced analytical and problem-solving skills, with the ability to articulate business impact clearly
- Strong experience using Python for data analysis and modelling
- Experience building and applying predictive models in a commercial context
- Comfortable working in a fast-paced environment with a high level of ownership and accountability
What They Offer
- Salary between £100,000 and £130,000 plus a comprehensive benefits package
- Hybrid working with a minimum of one day per week in the London office
- Exposure to high-value, business-critical projects with real visibility and influence
- A strong culture of learning, development and progression for high-performing data scientists
To Apply for this Job Click Here
Senior Data Scientist
London
£75000 - £90000
+ Data Science & AI
ContractLondon
To Apply for this Job Click Here
Senior Data Scientist (12 Month FTC)
London (Hybrid, 2 days per week)
The Company
They are a well established health and insurance technology organisation operating at significant scale, combining traditional insurance products with personalised, data driven services. Data Science is a core strategic capability, with strong executive support and a track record of deploying models and AI applications into live customer and internal workflows. The environment balances innovation with the rigour required in a regulated setting.
The Role
As a Senior Data Scientist focused on AI Engineering, you will take ownership of full lifecycle projects, from problem definition through to deployment and monitoring.
- Lead end to end delivery of AI and LLM based solutions across underwriting and insurance journeys
- Build and deploy agentic workflows, RAG pipelines, and LLM powered applications for internal and customer facing products
- Work across structured and unstructured data, collaborating closely with Machine Learning and GenAI specialists
- Own cloud based deployment, CI/CD, and productionisation of models and services
- Partner with product managers, engineers, and business stakeholders to shape requirements and scope projects
- Communicate progress and outcomes clearly, building trust through delivery and technical leadership
This role sits within a balanced team of traditional ML and GenAI specialists, with exposure to both classic modelling and modern AI use cases.
Your Skills and Experience
- Strong Data Science fundamentals and experience delivering classical machine learning models in production
- Hands on experience with LLMs, agentic workflows, and applied AI solutions
- Proven capability deploying models and AI services end to end, including monitoring and optimisation
- Solid engineering mindset with experience in cloud platforms, CI/CD, APIs, and containerised environments
- Confidence owning projects independently and leading delivery in cross functional teams
- Experience working in regulated, customer facing, or large scale data environments is beneficial
What They Offer
- Pension with up to 6 percent employer matching
- Private health and life insurance
- Hybrid working with a central London office location
- Strong likelihood of contract extension based on project delivery
How to Apply
If you are looking to own impactful AI projects in a mature, well supported Data Science environment, apply now to learn more.
To Apply for this Job Click Here
AI Engineer (FTC)
London
£70000 - £90000
+ Data Science & AI
PermanentLondon
To Apply for this Job Click Here
12 Month FTC Role
Senior Data Scientist (12 Month Fixed Term Contract)
London (Hybrid, 2 days per week in the office)
This is a rare opportunity to step into a senior, end to end role where you will take full ownership of high impact data science and AI projects during a critical period of transition. You will work across a genuinely mixed portfolio of traditional machine learning and modern generative AI, with real responsibility for design, deployment, and stakeholder outcomes.
The Role
You will join a blended Data Science and AI Engineering team, covering a maternity leave where projects are already live and business critical. You will own work end to end, from scoping and solution design through to production deployment and ongoing monitoring.
Key responsibilities include:
- Owning and delivering machine learning and AI projects from problem definition to production.
- Designing models from scratch across both structured and unstructured data use cases.
- Building and deploying LLM based solutions including summarisation and agentic workflows.
- Delivering traditional machine learning models using techniques such as gradient boosting.
- Working closely with product, engineering, and business stakeholders to define requirements and trade offs.
- Deploying models using cloud infrastructure, CI/CD pipelines, and MLOps best practices.
- Taking responsibility for monitoring, performance, and ongoing improvement of live models.
Your Skills and Experience
- Strong commercial data science experience with a solid grounding in classical machine learning.
- Hands on experience deploying and owning models in production environments.
- Good understanding of LLMs and modern AI approaches, or clear capability to apply them in production.
- Experience working with cloud platforms and deployment pipelines, with exposure to CI/CD and MLOps.
- Confident working end to end, from exploratory analysis through to scalable deployment.
- Comfortable operating in regulated or complex environments.
- Strong stakeholder management and communication skills, with the ability to translate business needs into technical solutions.
Please note, this is a 12 month FTC
To Apply for this Job Click Here
Customer Insights Strategy Lead
London
£60000 - £90000
+ Advanced Analytics & Marketing Insights
PermanentLondon
To Apply for this Job Click Here
Customer Insights and Strategy Transformation Lead
London, hybrid working with 2 days per week in the office. £60,000 to £90,000 plus 16 percent bonus.
This is a high visibility role for someone who wants to shape how customer insight influences strategy, change and commercial decision making. You will sit at the centre of analytics, BI and insight, with the remit and autonomy to build a customer insight capability that delivers real outcomes, not just reports.
The Company
They are a large, established organisation investing heavily in becoming more customer led and insight driven. Data, analytics and insight are seen as critical enablers of strategic transformation. Teams work collaboratively across analytics, marketing, product and technology, with strong senior sponsorship for insight-led decision making.
The Role
- Act as the strategic link between customer insight, central analytics, BI and self service reporting.
- Define the customer insight vision, operating model and prioritisation frameworks.
- Champion a customer first mindset and ensure insight informs strategy, change initiatives and day to day decisions.
- Work closely with marketing, product and data science teams to embed insight into delivery.
- Balance strategic direction with hands on delivery, including analysis, tooling and insight communication.
- Improve confidence in customer data as a strategic asset and uplift ways of working across insight teams.
Your Skills and Experience
- Strong commercial experience in customer analytics or customer insight leadership.
- Advanced SQL skills and strong experience with Power BI or equivalent BI tools.
- Python capability is highly desirable, particularly for advanced or predictive insight.
- Proven ability to translate data into clear, actionable business insight.
- Experience building or supporting predictive models or analytical frameworks.
- Strong stakeholder ownership, with a focus on delivery and impact over theory.
What They Offer
- Salary banding of £60,000 to £90,000, with a sweet spot in the mid to high £70,000s.
- 16 percent bonus.
- Hybrid working with two days per week in the London office.
- A highly visible role with genuine influence over how insight, data and analytics work together.
- The opportunity to build capability and shape something that lasts.
How to Apply
Apply now to find out more about this opportunity and how you could shape the future of customer insight in a business where your work will have real impact.
To Apply for this Job Click Here
Customer Insight Lead
London
£65000 - £80000
+ Advanced Analytics & Marketing Insights
PermanentLondon
To Apply for this Job Click Here
Customer Insight Lead
London/Bournemouth
60-80k
We are hiring a Customer Insights and Strategy Transformation Lead to define and connect the customer insight vision across analytics, BI and self service reporting. This role blends strategic direction with hands on delivery, ensuring insight directly shapes customer outcomes and commercial decisions.
This is a high visibility opportunity to sit at the centre of data, insight and decision making. You will have real influence over how customer insight is used across the organisation, with the mandate to improve ways of working, build scalable capability and raise confidence in data as a strategic asset.
The Company
We are working with a large, well established organisation operating in a highly regulated, customer focused environment. The business places growing importance on analytics and customer insight to drive strategy, transformation and performance. With teams based across multiple UK locations, they offer a collaborative culture and a strong commitment to hybrid working and long term investment in data capability.
The Role
- Own the customer insight vision and operating model, aligning analytics, BI and self service reporting.
- Act as the strategic bridge between data, technology, insight and business change.
- Work closely with analytics, insight and data science leaders to prioritise and deliver high impact insight.
- Translate complex analysis into clear, actionable recommendations for senior stakeholders.
- Champion a customer first mindset, embedding insight into strategy, transformation and day to day decisions.
- Remain hands on with analysis, tooling and insight delivery, not just oversight.
- Improve trust, governance and consistency in customer data and insight outputs.
Your Skills and Experience
- Strong commercial experience in customer analytics or customer insight leadership roles.
- Advanced SQL skills and strong experience using Power BI or similar BI tools.
- Working capability in Python for analysis, modelling or data exploration.
- Experience building or supporting predictive insight, models or analytical frameworks.
- Deep understanding of self service reporting and scalable insight delivery.
- Proven ability to own senior stakeholders and drive insight through to measurable impact.
What We Offer
- Hybrid working, typically two days per week in the office.
- A high impact role with scope to shape customer insight capability for the long term.
- Clear opportunity to influence strategy, transformation and analytics maturity.
How to Apply
Apply through Harnham to learn more about this opportunity and discuss your suitability in confidence.
To Apply for this Job Click Here
Senior Customer Insights Lead
London
£60000 - £90000
+ Advanced Analytics & Marketing Insights
PermanentLondon
To Apply for this Job Click Here
Customer Insight Strategy and Transformation Lead
London, Hybrid
Salary up to £90,000
This is a senior, high impact role for an insight leader who wants to shape how customer intelligence drives decision making at enterprise level. You will play a pivotal role in building scalable insight capability, embedding advanced analytics, and enabling the organisation to move from reactive reporting to proactive, strategic insight.
The Company
They are a large, complex organisation with a strong focus on customer outcomes and data driven transformation. Customer insight sits at the heart of their strategy, supporting commercial, operational and regulatory priorities. The business is investing heavily in analytics, AI and self service insight to unlock value and improve customer experience at scale.
The Role
- Lead the strategic enablement of a central insights capability, defining frameworks, standards and ways of working
- Architect and connect multiple internal and external data sources to create a holistic 360 degree customer view
- Drive development of robust data infrastructure, governance and self service analytics tools
- Lead advanced analytics and strategic insight initiatives, including predictive modelling and AI driven approaches
- Translate complex analytics into clear, actionable recommendations for senior stakeholders
- Champion innovation in tools, techniques and methodologies across customer analytics
- Coach and develop analytics capability across teams, raising overall analytical maturity
- Collaborate closely with senior leaders across insight, analytics and the wider business
Your Skills and Experience
- Strong commercial experience leading customer insight or analytics strategy in a complex organisation
- Deep expertise in data architecture, integration and connectivity across cloud platforms, SQL and APIs
- Advanced capability in analytics, predictive modelling, machine learning or AI driven insight
- Proven ability to design and maintain integrated customer data assets
- Confident communicator, able to influence senior stakeholders and translate insight into action
- Strong strategic thinking with the ability to connect insight to business impact
- Experience embedding analytics best practice and enabling self service insight
How to Apply
Apply now to explore how this Customer Insight Strategy and Transformation Lead role could be the next step in your analytics leadership career.
To Apply for this Job Click Here
Software Developer
London
£45000 - £50000
+ Data Engineering
PermanentLondon
To Apply for this Job Click Here
SOFTWARE DEVELOPER
£50,000
LONDON
Our client is looking for a Software Developer to join a well‑established, digital‑first organisation with a heavy investment in data, analytics, and modern engineering practices. This is a growth role for someone who enjoys writing clean, well‑structured Python, taking ownership of production code, and working in an environment where adaptability and collaboration really matter.
THE COMPANY
A UK‑based, consumer‑facing organisation operating at national scale, with millions of customers and a strong reputation for being technology‑led. The business has been investing heavily in digital transformation, growing headcount significantly over the past two years and continuing to build out its engineering and data capabilities.
THE ROLE
- Design, develop, and maintain Python code supporting core internal platforms
- Take ownership of a significant portion of the codebase, progressing changes from development through to deployment
- Write clean, well‑structured, object‑oriented Python code following best practices
- Use GitHub for version control, code reviews, and pull requests
- Work collaboratively with engineers, analysts, and data professionals across the wider team
- Adapt to different ways of working and contribute positively to technical discussions and feedback loops
YOUR SKILLS AND EXPERIENCE
- 2-3 years’ experience working as a Software Developer (experience level flexible for strong candidates)
- Strong Python development skills with a good grounding in OOP principles
- Experience working with version control (Git) and collaborative coding workflows
- Curious, adaptable, and keen to learn within a complex technical environment
SALARY AND BENEFITS
- Base salary: £50,000
- Mostly remote working (fortnightly collaboration days in London)
- Stable, well‑funded business with long‑term technical investment
- Opportunity to grow responsibility and technical ownership over time
- Exposure to high‑quality engineering and data practices
To Apply for this Job Click Here
Data Scientist
£50000 - £90000
+ Risk Analytics
PermanentEngland
To Apply for this Job Click Here
Data Scientist
Salary
£60,000 – £90,000 + Bonus + Pension
Location + work pattern
London – Hybrid working (1-2 days per week remote)
THE COMPANY
- A well-funded, technology-led insurance business operating across multiple European markets.
- Known for a highly data-driven culture and strong investment in analytics and engineering.
- Experiencing rapid growth in the UK, with analytics at the core of commercial decision-making.
THE ROLE
This role sits within the pricing and underwriting analytics function, applying data science to optimise pricing strategy and improve market competitiveness. You will work on complex, real-world problems with clear commercial impact.
Specifically, you can expect to be involved in:
- Developing and enhancing predictive models used within pricing and risk frameworks.
- Exploring and integrating new data sources to improve model performance.
- Simulating and analysing customer behaviour, including price sensitivity and risk outcomes.
- Collaborating closely with analytics, engineering and wider commercial teams to deliver insight.
YOUR SKILLS AND EXPERIENCE
- Strong quantitative background (e.g. Mathematics, Physics or similar) from a top univeristy is a must.
- Experience using Python for data analysis or modelling, or strong academic exposure.
- Ability to approach problems logically, explaining reasoning clearly and methodically.
- Hands-on data science or modelling experience in a retail insurance environment.
- Clear communication skills and motivation to work in a fast-paced, high-expectation setting.
THE BENEFITS
- Competitive salary aligned to experience and seniority.
- Hybrid working model with flexibility built in.
- High level of ownership and exposure to meaningful projects.
- Opportunity to develop rapidly within a technically strong analytics team.
- Collaborative, performance-driven culture with a strong focus on learning and progression.
THE PROCESS
- Initial CV review followed by a take-home mathematical and logic-based assessment.
- Short introductory interview focused on motivation and role fit.
- In-depth technical interview centred on reasoning, problem-solving and applied thinking.
- Final-stage competency-based interview.
- Offer!
HOW TO APPLY
Please register your interest via the apply link on this page.
To Apply for this Job Click Here
Data Scientist
London
£60000 - £75000
+ Data Science & AI
PermanentLondon
To Apply for this Job Click Here
Data Scientist
London, 60,000 to 70,000 plus benefits, hybrid working, no sponsorship
This role offers the chance to step into a high‑impact data science position within a fast‑moving pricing environment where analytics directly influences commercial outcomes. You will work on complex, real‑world modelling problems with genuine ownership and visibility, rather than supporting work at the edges of the business.
The Company
They are a well‑established European insurance business operating in a highly data‑driven and technology‑led environment. With strong backing and ambitious growth plans in the UK, analytics plays a central role in how they compete and respond to market change. The culture is collaborative, fast paced and focused on building advanced analytical capability in‑house.
The Role
- Work within a pricing and underwriting data science team, building and improving predictive models that influence commercial strategy.
- Develop pricing and risk models that balance competitiveness with profitability.
- Explore, evaluate and integrate new data sources to enhance model performance.
- Use statistical thinking, logic and reasoning to simulate customer behaviour, including risk frequency and price sensitivity.
- Communicate insights clearly and influence decision‑making across technical and non‑technical stakeholders.
- Partner closely with machine learning engineers and other data professionals to deploy robust, scalable solutions.
Your Skills and Experience
- Strong commercial experience in data science or statistical modelling within any industry.
- Advanced Python skills for analysis and modelling.
- A solid grounding in statistical or mathematical concepts and applied problem solving.
- Confidence reasoning through complex problems and explaining your approach clearly.
- Experience working with end‑to‑end modelling problems, from concept through to impact.
- You must already have the right to work in the UK, as sponsorship is not available.
What They Offer
- Salary ranging from 60,000 to 75,000 depending on experience.
- Hybrid working with 1 to 2 days per week in the London office.
- High levels of autonomy and ownership over meaningful projects.
- Exposure to a wide range of analytical challenges in a fast‑growing business.
- Clear opportunities for progression as the UK data function continues to scale.
How to Apply
Apply now to learn more about this data science opportunity within a high‑impact pricing team.
To Apply for this Job Click Here
Data Scientist
London
£45000 - £50000
+ Data Science & AI
PermanentLondon
To Apply for this Job Click Here
Data Scientist
London, 45,000 to 50,000 plus benefits, hybrid working 1 to 2 days per week, No sponsorship available
This is a standout opportunity for a high‑potential graduate to launch a data science career in a business where analytics directly shapes commercial decisions. You will join a pricing team known for developing early‑career talent, offering real ownership, structured learning and exposure to impactful projects from day one.
The Company
They are a fast‑growing European motor insurance business operating in a highly data‑driven, technology‑led environment. Having successfully scaled across multiple markets, they are now investing heavily in their UK analytical capability. The culture is ambitious, collaborative and focused on hiring bright, curious people with strong problem‑solving ability.
The Role
- Work within a data science function aligned to pricing and underwriting.
- Build and improve predictive models to support pricing competitiveness and risk management.
- Use logical reasoning and statistical thinking to simulate customer behaviour, including risk and price sensitivity.
- Explore and assess new data sources to enhance modelling performance.
- Communicate your approach and reasoning clearly when solving complex problems.
- Collaborate closely with experienced data scientists and machine learning engineers.
Your Skills and Experience
- A Masters degree in Mathematics, Physics or a closely related quantitative subject.
- Strong academic performance with a 2.1 or above.
- Excellent logical, analytical and problem‑solving skills.
- Motivation, curiosity and a clear interest in applied data science.
- Ability to explain your thinking and approach when tackling unfamiliar problems.
- Any exposure to Python or analytical programming is beneficial but not essential for graduates.
- You must already have the right to work in the UK, as sponsorship is not available for this role.
What They Offer
- Salary of 45,000 to 50,000.
- Hybrid working with 1 to 2 days per week in the London office.
- Structured training, mentorship and clear development pathways within data science.
- High levels of responsibility and ownership early in your career.
- The chance to grow within a business that genuinely values data and invests in its people.
How to Apply
Apply now to learn more about this graduate data science opportunity within a high‑performing pricing team.
To Apply for this Job Click Here
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FAQ:
What does a Data Analyst do in insurance?Â
- A data analyst in the insurance industry plays a vital role in extracting meaningful insights from vast datasets to inform decision-making, improve operational efficiency, and manage risk effectively.
How much do insurance analysts make in the UK?
- The average salary for an Insurance Analyst in the UK is £33,819 per year. However, Salaries may differ by location, with analysts in London typically earning higher incomes due to the city's higher cost of living.
What are the different types of insurance analysts?
There are several different types of insurance analysts who specialise in various areas.
- Fraud Analysts: These analysts specialise in identifying and preventing fraudulent insurance claims. They do this through the use of data analysis and investigative techniques to detect suspicious activities.
- Claims Analysts: This type of analyst will investigate claims made by policyholders. They verify the validity of the claims and assess the extent.
- Actuarial Analyst: This type uses mathematical and statistical models to assess and predict risk. This helps insurance companies set rates & estimate future liabilities.Â
- Market Research Analysts: These Analysts study industry trends, customer behaviour & their competitors. All so that they can provide insights for their insurance companies. Which use them to identify new market opportunities & marketing strategies.
- Risk Analysts: Asses and manage various types of risks that insurance companies face, this includes financial, operational & market risks. A big part of their responsibilities is developing risk mitigation strategies to safeguard the company's financial stability.Â
What is an insurance analytics platform?
- A risk management software solution, specially designed to help insurance companies leverage data analytics to improve their decision-making, manage risks more effectively and manage risks more efficiently. The data provides analysts with the insights they need to inform key stakeholders of potential threats to and opportunities for the business.
How big is the insurance analytics market?
- As of 2022 & on a global scale, the insurance analytics market was valued at $11.71 Billion. It is expected to grow at a rate of 15.4% from 2023 through 2029. With an eventual value of $31.92 Billion.
How is data analytics transforming the insurance industry?Â
- The main aspect of data analytics that has proven to be game-changing is Cloud Computing. Cloud computing has and continues to improve the performance of analytics in real-time and in greater depth. Â
Why do we need data analytics in the insurance industry?
- With data analytics, an insurer enables itself to optimise every single aspect of its business using insights from data. This is known as data-driven decision-making.Â
Industry Trends
In the world of insurance, stability has traditionally been the cornerstone, allowing for predictable risk assessment and steady growth. However, the once steadfast foundations of this industry are undergoing a profound transformation. Over the past few years, a series of short-term crises have shaken the very core of insurance. From a global pandemic to political unrest, supply chain disruptions, and extreme weather events, the landscape is evolving in ways unimaginable just two decades ago.
These short-term crises are not isolated incidents but rather symptomatic of larger, long-term trends. Previously, we referred to these trends as STEEP factors, encompassing Social, Technological, Economic, Environmental, and Political influences. Today, their impact is more pronounced than ever. Social instability, technological disruption, shifting demographics, and climate change are converging to create a fractured world. Insurers now face a daunting array of intensifying risks, both in terms of variety and frequency.
Consequently, these developments have brought about significant changes within the insurance industry itself. Let's explore some of these transformative shifts:
Market Evolution
The traditional insurance market is undergoing a seismic shift. The rise of digital channels and an expanded network of distribution points, including partnerships and embedded options, is disintermediating markets. This proliferation of policy options and easier access challenges the established dominance of carriers. Barriers to entry are diminishing, setting the stage for increased competition and innovation.
Operational Adaptation
Even before the pandemic, insurers were grappling with substantial changes across their operations. In multiple PwC CEO surveys, insurance leaders consistently identified disruption as their primary challenge. The pandemic further accelerated these changes, pushing the workforce and customer interactions into the virtual realm. This shift stressed various functions, including IT, HR, and sales, while upending many established assumptions and behaviours. The industry responded by experimenting with new approaches, but a sense of caution lingers as insurers remain vigilant for unforeseen challenges.
Technological Revolution
Insurers are striving to become tech-enabled, leveraging data from multiple sources to rapidly assess and price risk. Additionally, they aim to provide seamless customer experiences, offering information and insurance precisely when clients need it. Achieving this vision requires a flexible technological infrastructure and a strategic IT function. While progress is evident, most carriers still have a substantial distance to cover before becoming truly tech-enabled, as opposed to merely 'digital.'
Environmental and Social Responsibility
Environmental, Social, and Governance (ESG) considerations are now central to the insurance industry's continued relevance. Compliance with reporting requirements and maintaining a positive brand image are just the tip of the iceberg. Insurers are extending their responsibilities to help clients and society at large mitigate natural and human catastrophes, cybercrime, and other loss incidents. By doing so, they reduce claims, boost profitability, and ensure their long-term viability as carriers.
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These proactive approaches not only reduce claims but also boost profitability and ensure carrier viability. As insurers grapple with these challenges, we anticipate four likely approaches:
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Incremental Change: This aligns with the current and historical norm for most carriers. They adapt incrementally, often reactively, in response to STEEP developments. This approach involves modernizing certain key operational aspects, such as claims processing and customer service, albeit without a comprehensive vision for how cloud and digital transformation can enhance broader business and operations. Incremental change also includes defending or expanding market share, primarily by competing on price and refining loss mitigation and prevention measures. It often involves short-term cost-cutting and a slow adoption of data-driven service improvements. This approach may lack full funding and consistent C-suite support.
- While it modernizes and enhances certain critical operational facets, such as claims processing, and customer service features like autopay and self-service options, it frequently falls short in adopting a comprehensive enterprise-wide perspective on how cloud and digital transformation can truly elevate the broader spectrum of business and operational functions
- Safeguards its market position and bolsters its brand image among specific customer segments, usually by engaging in price-based competition.
-
Enhances the improvement of loss prevention and mitigation on the periphery.
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Reduces costs usually by implementing short-term expense reductions.
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Utilizes data at a leisurely pace when it comes to enhancing service experiences.
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Employs data at a relaxed rate when improving service encounters.
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The Customer-First Approach: Some insurers are restructuring their business and operating models to place the customer at the forefront. This entails aligning offerings and services with evolving customer needs over time. on the customer's needs. This entails fashioning personalized, all-encompassing insurance packages right at the moment of purchase, while seamlessly eliminating any friction by amalgamating service and support across our array of offerings.
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The principle that carrier and customer success are indistinguishable is fully endorsed by business and operating models.
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Encompasses a wide range of consumer and risk data sourced from sensors, telematics, and unstructured data to tailor coverage, offer a smooth service experience, mitigate risks, and earn the confidence of customers.
- Provides user-friendly and educational AI-driven insurance and financial solutions for various stakeholders, including employers and their staff (group), enterprises (both commercial and personal lines), individual clients (across all coverage categories), and agents (across all business sectors).
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Disseminates data from the mentioned applications and others both within the company and to pertinent collaborators in order to uphold a live grasp of customer requirements, actions, and risk assessments.
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Aids policyholders and the community in proactively preventing losses by shifting from relying on probability-based risk management to adopting a deterministic approach. This, in turn, leads to a decrease in claim payments and an enhancement in overall profitability.
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Pragmatic Evolution: Insurers adopting this approach orchestrate coverages, services, and support in response to changing customer requirements, offering a flexible and adaptable insurance experience.
-
Enhances the potential of cloud and digital advancements by reinventing the customer journey, establishing a beneficial cycle of support between technological empowerment, dissemination, and customer assistance.
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Facilitates the process of transformation by optimizing crucial processes and mitigating risks in order to boost income, foster business creativity, encourage expansion, and enhance adaptability.
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Explorations involving interconnected, multifaceted points of engagement, encompassing ecosystems and incorporated insurance.
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Tailors insurance policies and offers convenient (self-)service through the utilization of consumer and market information to craft suitable, AI-informed options for specific customer groups.
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To optimize the efficiency of AI from its inception and throughout its lifecycle, an automated framework is employed to gauge the effectiveness of AI bots.
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Ensuring customer retention involves simplifying policy renewal procedures and showing careful consideration regarding the mode and frequency of communication.
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Radical Reinvention: A few visionary insurers are creating unique business and operating models that redefine insurance and minimize risk. They aim to revolutionize the industry rather than merely adapt to change.
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Partnerships play a crucial role in our strategic approach due to the reduced barriers to entry and the expanded array of consumer touchpoints, coverages, and coverage options. This requires us to move away from conventional business and operating models.
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Consequently, inclusion is effortlessly integrated into virtually any transaction's point of sale through collaborative alliances and interconnected systems.
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Utilizes cutting-edge artificial intelligence that functions discreetly, preemptively identifying the requirements of customers to the extent that it can adjust insurance policies appropriately with minimal or even no intervention from the purchaser.
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Utilizes the information within its reach to collaborate closely with policyholders, communities, governmental, and private entities in order to proactively improve the factors contributing to and thwart the occurrence of natural disaster losses and cyberattacks.
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