Lead Data Scientist

AXA UK
Bolton
3 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist - Deep Learning Practitioner

Lead Data Scientist - Deep Learning Practitioner

Lead Data Scientist - Deep Learning Practitioner

Description

Are you passionate about turning data into actionable insights? Do you thrive on leading innovative projects that make a real difference? AXA Commercial is seeking a talented Lead Data Scientist to join our dynamic Commercial Data Science team. You'll be at the forefront of applying advanced analytics and artificial intelligence to create tangible value for our customers and the business. Your expertise will help shape data-driven strategies, optimise processes, and enhance customer experiences across the organisation.


As our Lead Data Scientist, you'll have the chance to collaborate with a skilled team of Data Scientists, Analysts, and Engineers, harnessing cutting‑edge tools like Azure Databricks to uncover insights and develop scalable solutions. This is an exciting opportunity to explore diverse datasets, solve complex use cases, and lead end‑to‑end projects that drive strategic decision‑making and operational excellence.


At AXA we work smart, empowering our people to balance their time between home and the office in a way that works best for them, their team and our customers. You'll work at least two days a week (40%) away from home, moving to three days a week (60%) in the future. Away from home means either attendance at one of our office locations, visiting clients or attending industry events. We’re also happy to consider flexible working arrangements, which you can discuss with Talent Acquisition.


What you’ll be doing:

  • Act as a technical leader in data science and AI, setting standards and guiding best practices.
  • Design, build, and productionise machine learning models and analytics products that deliver business value.
  • Collaborate with senior stakeholders to identify opportunities, define requirements, and translate business challenges into data‑driven solutions.
  • Oversee the performance and reliability of deployed models, ensuring continuous improvement and scalability.
  • Mentor and support junior Data Scientists, fostering a culture of learning and innovation.
  • Apply advanced techniques such as predictive modelling, generative AI, and statistical analysis to solve complex problems.
  • Work with cloud‑based platforms and tools such as Azure, Databricks, and ML services, to deliver robust solutions.
  • Ensure models are embedded into operational processes through MLOps best practices.
  • Communicate insights and recommendations effectively to technical and non‑technical audiences.

Due to the number of applications we expect to receive for this role, we reserve the right to close this advert earlier than the listed closing date to ensure we’re able to effectively manage interest. Therefore, if you’re interested in joining us at AXA, please don’t hesitate to apply.


What you’ll bring:

  • Proven experience in delivering end‑to‑end data science projects, from ideation to production.
  • Strong technical expertise in machine learning, AI, and advanced analytics techniques.
  • Proficiency in Python, R, SQL, and familiarity with cloud and ML tools such as Azure, Databricks, and Vector DB.
  • Experience with MLOps and integrating models into production environments.
  • Ability to assess model performance and apply techniques like data mining, predictive modelling, and generative AI.
  • Skilled in stakeholder engagement and translating business needs into actionable solutions.
  • Collaborative mindset with the ability to mentor and guide junior team members.
  • Commitment to continuous learning and staying ahead of developments in the data science landscape.

As a precondition of employment for this role, you must be eligible and authorised to work in the United Kingdom.


What we offer:

  • Competitive annual salary dependent on experience
  • Annual company & performance‑based bonus
  • Contributory pension scheme (up to 12% employer contributions)
  • Life Assurance (up to 10 x annual salary)
  • Private medical cover
  • 28 days annual leave plus Bank Holidays
  • Opportunity to buy up to 5 extra days leave or sell up to 5 days leave
  • Wellbeing services & resources
  • AXA employee discounts

To apply, click on the ‘apply for this job’ button, you’ll then need to log in or create a profile to submit your CV. We’re proud to be an Equal Opportunities Employer and don’t discriminate against employees or potential employees based on protected characteristics. If you have a long‑term condition or disability and require adjustments during the application or interview process, we’re proud to offer access to the AXA Accessibility Concierge. For our support, please send an email to leanne.white@axa‑insurance.co.uk.
#LI-DNI


Who we are:

AXA Commercial protects businesses, from multinationals to micro start‑ups, giving them the confidence to thrive. We’re currently making our biggest ever investment to develop the expertise and skills we need to be the best. We’re a vibrant community where everyone is supported to learn, develop, and take ownership of their work.


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.