At a Glance
- Tasks: Develop predictive models and insights using data to support insurance risk and pricing decisions.
- Company: Join Experian, a global leader in data and technology with a people-first culture.
- Benefits: Enjoy a competitive salary, hybrid working, generous leave, and comprehensive health benefits.
- Other info: Be part of an award-winning workplace focused on diversity, collaboration, and personal growth.
- Why this job: Make a real impact by innovating with data and shaping the future of insurance.
- Qualifications: Experience in R or Python, SQL skills, and a passion for data science.
The predicted salary is between 50000 - 65000 £ per year.
Experian is a global data and technology company, powering opportunities for people and businesses around the world. We help to redefine lending practices, uncover and prevent fraud, simplify healthcare, create marketing solutions, and gain deeper insights into the automotive market, all using our unique combination of data, analytics and software.
We're looking for a Data Scientist to join our Data Science team, working on a data and analytics product that supports insurers with property risk and pricing decisions. This is a hands-on role with a strong focus on modelling and feature innovation, where you'll develop new predictive capabilities while working with real-world datasets used in live decisioning environments reporting into our Head of Categorisation.
What You'll Do:
- Develop predictive features and models, applying statistical and machine learning techniques to support risk assessment, pricing, and decisioning.
- Explore and extract insights from structured, unstructured, and geospatial datasets to unlock new data value.
- Improve existing features and models, identifying opportunities to enhance performance and predictive power.
- Lead the quality and performance of production data outputs, ensuring accuracy, reliability, and consistency across API and flat-file delivery.
- Monitor and improve data pipelines and feature performance, identifying issues and opportunities for automation.
- Analyse and improve the completeness and integrity of the underlying data estate, driving remediation where needed.
- Use generative AI tools to improve workflows, accelerate analysis, and enhance product development processes.
- Work cross-functionally with product, engineering, and commercial teams to translate data insights into product improvements and innovation.
- Support client-facing teams by explaining data behaviour, feature logic, and modelling outputs where needed.
- Document methodologies, assumptions, and transformations to ensure transparency, reproducibility, and knowledge sharing.
Qualifications:
- Experience in R (preferred) or Python for data analysis, modelling, and feature development.
- SQL skills and an understanding of relational databases.
- Experience working with large datasets, including property, geospatial, or risk-related data.
- Understanding of statistical modelling, feature engineering, and machine learning techniques.
- Experience building and maintaining data pipelines (data cleaning, validation, transformation).
- Comfortable working with APIs, flat-file delivery, and version-controlled codebases (e.g., Git).
- Familiarity with geospatial data (e.g., spatial joins, shapefiles, geocoding) and/or cloud environments (AWS/Azure).
- Commercial experience in a data science or advanced analytics role.
- Background in insurance, risk, credit, or other regulated/data-rich industries is advantageous.
- Analytical mindset with a focus on data quality in production environments.
- Comfortable balancing innovation (modelling, feature development) with operational ownership (data delivery, pipelines).
Additional Information:
- Hybrid working, 2 days a week in our Glasgow office.
- Great compensation package and discretionary bonus.
- Core benefits include pension, Bupa healthcare, sharesave scheme and more.
- 25 days annual leave with 8 bank holidays and 3 volunteering days. You can purchase additional annual leave.
Experian is proud to be an Equal Opportunity and Affirmative Action employer. Innovation is an important part of Experian's DNA and practices, and our diverse workforce drives our success.
Data Scientist – Data & Analytics Products in Glasgow employer: Experian
Experian is an exceptional employer, offering a dynamic work culture that prioritises employee well-being and professional growth. With a strong focus on diversity, equity, and inclusion, employees enjoy a hybrid working model, competitive compensation, and a comprehensive benefits package, including generous annual leave and opportunities for volunteering. As a leader in data and technology, Experian empowers its team to innovate and excel in their roles, making it a rewarding place to build a meaningful career in Glasgow.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist – Data & Analytics Products in Glasgow
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with current Experian employees on LinkedIn. A friendly chat can sometimes lead to job opportunities that aren't even advertised!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data science projects, especially those involving predictive modelling or machine learning. This will give you an edge and demonstrate your hands-on experience.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills and understanding of the insurance domain. Be ready to discuss how you've tackled real-world data challenges and how you can contribute to Experian's innovative projects.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, it shows you're genuinely interested in joining the Experian team and contributing to our mission.
We think you need these skills to ace Data Scientist – Data & Analytics Products in Glasgow
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter for the Data Scientist role. Highlight your experience with R or Python, and any relevant projects that showcase your skills in data analysis and modelling. We want to see how you can bring value to our team!
Showcase Your Skills:Don’t just list your skills; demonstrate them! Include specific examples of how you've used statistical modelling, feature engineering, or machine learning techniques in past roles. This helps us understand your practical experience and how it aligns with what we do.
Be Clear and Concise:When writing your application, keep it clear and to the point. Use bullet points where possible to make it easy for us to read. We appreciate a well-structured application that gets straight to the heart of your qualifications and experiences.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets to the right people. Plus, you’ll find all the details about the role and our company culture there!
How to prepare for a job interview at Experian
✨Know Your Data Tools
Make sure you're well-versed in R or Python, as these are crucial for the role. Brush up on your SQL skills too, since you'll be working with large datasets and relational databases. Being able to demonstrate your proficiency in these tools will show that you're ready to hit the ground running.
✨Understand the Business Context
Familiarise yourself with the insurance industry and how data science plays a role in risk assessment and pricing decisions. This knowledge will help you articulate how your skills can directly contribute to Experian's goals, making you a more attractive candidate.
✨Prepare for Technical Questions
Expect questions about statistical modelling, feature engineering, and machine learning techniques. Be ready to discuss your past projects and how you've applied these concepts in real-world scenarios. Practising common technical interview questions can give you the confidence you need.
✨Showcase Your Problem-Solving Skills
During the interview, highlight your analytical mindset and ability to improve data quality and performance. Share examples of how you've identified issues in data pipelines or enhanced predictive models. This will demonstrate your proactive approach and commitment to excellence.
