At a Glance
- Tasks: Use data to solve business challenges and build AI models for impactful insights.
- Company: Join AXA, a global leader in insurance and financial services.
- Benefits: Competitive salary, flexible working, and opportunities for continuous learning.
- Other info: Dynamic team culture with excellent career growth potential.
- Why this job: Make a real difference with data while growing your skills in a supportive environment.
- Qualifications: Degree in a quantitative field and experience with machine learning techniques.
The predicted salary is between 35000 - 45000 £ per year.
Locations: AXA House 4 The Parklands, Bolton, GB, BL6 4SD; Redhill. Salary: £35,000 to £45,000 dependent on experience.
About AXA: AXA is a global leader in insurance and financial services, dedicated to helping customers protect what matters most to them. As the sixth-largest insurance company in the world, we provide a wide range of services, including health, car, home, and business insurance. We support millions of customers worldwide, helping them navigate life's uncertainties with confidence.
AXA UK Support Functions look after our three customer-facing business units, providing the infrastructure and expertise to make sure we can be there for our customers.
Job overview: Join us as a Data Scientist and help solve tough business challenges with data-driven solutions. Using our cloud-based data lake, you'll build advanced machine learning and AI models to improve how the organisation understands and uses data. You'll explore and question data, turning insights into opportunities and supporting smarter decisions. Working alongside your colleagues, you'll develop stories from data that are easy to understand and impactful. We encourage continuous learning through experimentation, helping you grow your skills and business understanding. You’ll deliver valuable insights, diagnose issues, and communicate findings clearly to support business success and stakeholder confidence.
Responsibilities:
- Elicit, specify, and document requirements for straightforward subject areas, ensuring clear boundaries and managing agreed deliverables.
- Identify, validate, and leverage simple internal and external data sets generated from non-complex processes.
- Develop, with guidance, predictive and real-time model-based insights to add value and support decision-making.
- Find, acquire, clean, and integrate data to ensure it is fit for purpose, with support as needed.
- Collaborate with stakeholders to explore data, formulate hypotheses, and use models and analytic tools to uncover insights.
- Apply a range of basic analytical techniques, including data mining, pattern matching, forecasting, visualisation, and simple machine learning.
- Independently develop basic machine learning models to generate insights, predict behaviours, and create value.
- Present data insights visually and creatively to help both technical and non-technical audiences understand findings and support decision-making.
Qualifications:
- Degree in computer science, mathematics, statistics, operations research, or a related quantitative discipline.
- Proven experience in applying and evaluating machine learning techniques to new datasets and problems using programming languages such as Python and SQL within a regulated environment.
- Skilled in identifying issues within machine learning systems and data and making practical recommendations for improvements.
- Knowledge of cloud and machine learning platforms such as Azure, ML services, and Databricks, with awareness of their applications.
- Able to produce clear reports and publish model outputs that meet customer requirements and adhere to organisational standards.
- Capable of designing, coding, testing, documenting, and refactoring moderately simple programs or scripts, ensuring high-quality and well-engineered results.
- Familiar with advanced analytical techniques including data and text mining, pattern matching, forecasting, semantic and sentiment analysis, network and cluster analysis, neural networks, and more.
- Demonstrated ability to work effectively as part of a data science team, engaging with users to prototype, refine, and monitor progress, and to identify and resolve issues during development activities.
Work arrangements: 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 attending the office, visiting clients or attending industry events. We’re also happy to consider flexible working arrangements, which you can discuss with Talent Acquisition.
Other information: As a precondition of employment for this role, you must be eligible and authorised to work in the United Kingdom.
Data Scientist in Redhill employer: Women in Data®
AXA is an exceptional employer, offering a dynamic work culture that fosters continuous learning and innovation. As a Data Scientist, you'll have the opportunity to work with cutting-edge technology in a supportive environment that values collaboration and personal growth. With flexible working arrangements and a commitment to employee development, AXA empowers you to thrive both professionally and personally while making a meaningful impact in the insurance industry.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist in Redhill
✨Tip Number 1
Network like a pro! Reach out to current employees at AXA on LinkedIn or through industry events. A friendly chat can give you insider info and maybe even a referral!
✨Tip Number 2
Show off your skills! Prepare a portfolio of your data science projects, especially those using Python and SQL. Bring them up in interviews to demonstrate your hands-on experience.
✨Tip Number 3
Practice your storytelling! When discussing your data insights, make sure you can explain complex concepts in simple terms. This will impress both technical and non-technical folks.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining AXA.
We think you need these skills to ace Data Scientist in Redhill
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Data Scientist role. Highlight your experience with machine learning, data analysis, and any relevant programming skills like Python and SQL. We want to see how your background aligns with what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to tell us why you're passionate about data science and how you can contribute to AXA. Be sure to mention specific projects or experiences that showcase your skills.
Showcase Your Projects:If you've worked on any interesting data science projects, make sure to include them in your application. Whether it's a personal project or something from your previous job, we love seeing practical examples of your work!
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and you'll be able to keep track of your application status. Plus, we love seeing candidates who take the initiative!
How to prepare for a job interview at Women in Data®
✨Know Your Data
Before the interview, brush up on your knowledge of data science concepts and techniques. Be prepared to discuss how you've applied machine learning methods in past projects, especially using Python and SQL. This will show that you can hit the ground running at AXA.
✨Showcase Your Problem-Solving Skills
Think of specific examples where you've tackled complex data challenges. Be ready to explain your thought process and the steps you took to derive insights from data. This will demonstrate your ability to turn data into actionable solutions.
✨Communicate Clearly
Practice presenting your findings in a way that's easy for both technical and non-technical audiences to understand. Use visual aids if possible, as this aligns with the role's requirement to present data insights creatively and effectively.
✨Engage with the Team
During the interview, express your enthusiasm for collaboration. Discuss how you've worked with stakeholders in the past to refine hypotheses and develop models. This shows that you're not just a lone wolf but a team player who values input from others.
