Data Scientist – Data & Analytics Products at Experian

Glasgow, Scotland, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

27 Aug, 26

Salary

0.0

Posted On

29 May, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

R, Python, SQL, Machine Learning, Statistical Modelling, Feature Engineering, Data Pipelines, Geospatial Analysis, API Integration, Git, AWS, Azure, Data Cleaning, Predictive Modelling, Generative AI, Data Validation

Industry

Information Services

Description

Company Description 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 also assist millions of people to realize their financial goals and help them save time and money. We invest in people and new advanced technologies to unlock the power of data. As a FTSE 100 Index company listed on the London Stock Exchange (EXPN), we have a team of 22,500 people across 32 countries. Our corporate headquarters are in Dublin, Ireland. Learn more at experianplc.com. Job Description 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. Alongside this, you'll play an important role in maintaining and improving the performance, reliability, and quality of our production data outputs. While you'll initially focus on an insurance-focused product, this role will expand over time, offering the opportunity to contribute to new products and data services across other domains such as credit risk and affordability. 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 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. We take our people agenda very seriously and focus on what matters; DEI, work/life balance, development, authenticity, collaboration, wellness, reward & recognition, volunteering... the list goes on. Experian's people first approach is award-winning; World's Best Workplaces™ 2024 (Fortune Top 25), Great Place To Work™ in 24 countries, and Glassdoor Best Places to Work 2024 to name a few. Check out Experian Life on social or our Careers Site to understand why. 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. Everyone can succeed at Experian and bring their whole self to work, irrespective of their gender, ethnicity, religion, colour, sexuality, physical ability or age. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity. Experian Careers - Creating a better tomorrow together Find out what its like to work for Experian by clicking here #LI-Hybrid This is a hybrid remote/in-office role. Experian Careers - Creating a better tomorrow together Find out what its like to work for Experian by clicking here Employee Status: Regular Role Type: Hybrid Department: Analytics Schedule: Full Time

Responsibilities

Develop predictive features and machine learning models to support property risk and pricing decisions for insurers. Maintain the quality and reliability of production data outputs and optimize data pipelines for API and flat-file delivery.