Data Scientist - Remote

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 work towards their financial goals and help them save time and money.

We operate across a range of markets, from financial services to healthcare, automotive, agribusiness, insurance, and many more industry segments.

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 creative Data Scientist to join Experian Automotive and shape the future of data-driven decision-making. You'll design and implement advanced analytics solutions, standardize and automate data processes, and develop statistical, machine learning, and forecasting models that power business intelligence and client insights. You'll create next-generation algorithms, build intuitive data visualization tools, and use new technologies—including generative AI and advanced modeling techniques—to solve challenges. Collaborating across Finance, Product, Operations, and Technology teams, you'll guide automation, optimize analytical workflows, and deliver relevant insights that influence strategic decisions. This is an opportunity to push boundaries, transform data into intelligence, and accelerate innovation across the organization. You will report to Experian Automotive's Vice President of Product Management.

You'll have opportunity to:

• Build scalable data ecosystems to power advanced analytics, predictive modeling, and thoughtful decision-making.
• Lead forecasting and scenario modeling by integrating dynamic inputs, validating assumptions, and delivering forward-looking insights that anticipate risks and opportunities.
• Automate analytical workflows to accelerate speed-to-insight and reduce manual intervention.
• Use large-scale structured and unstructured data to uncover patterns, create relevant insights, and develop predictive and prescriptive models.
• Design, deploy, and operationalize advanced machine learning solutions—including deep learning, graph-based models, and reinforcement learning—to solve complex business and customer challenges.
• Maintain model development pipelines while driving proof-of-concept programs and rapid experimentation.
• Partner with teams to define high-impact use cases and embed data science solutions into strategic decision-making.
• Establish scalable data and model governance practices to ensure reliability, transparency, and continuous improvement.
• Use modern analytics and cloud platforms (e.g., Python, AWS, Databricks, or Tableau) to find the latest solutions.
• Improve algorithmic innovation by refining data analysis techniques, expanding pattern analytics, and generalizing methods for broader data flow coverage.
• Develop intuitive data visualizations and interactive tools to communicate complex insights and increase adoption across the organization.

Qualifications

• Education: Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field.
• Technical Expertise: Proficiency in Python and experience with machine learning frameworks such as scikit-learn, XGBoost, or PyTorch; familiarity with SQL and statistical tools (e.g., R, SAS, or MATLAB).
• AI & Advanced Modeling: Hands-on experience with generative AI tools, large language models (LLMs), and advanced machine learning techniques including supervised, unsupervised, and time-series modeling.
• Cloud & Deployment: Ability to deploy and scale models in production using cloud platforms (AWS, GCP, Azure) and tools like Databricks; experience with AWS services such as S3, Redshift, SageMaker, EMR, Kinesis, Lambda, or EC2.
• Data Mastery: Experienced in analyzing complex datasets and developing predictive models, experimental designs, and analytic plans to uncover insights and determine causal relationships.
• Statistical: Understanding of statistical modeling, forecasting, and performance optimization for machine learning algorithms.
• Collaboration & Communication: Ability to translate complex technical concepts into clear, relevant insights for diverse team members.
• Innovation and Execution: Experience managing projects end-to-end, delivering impactful solutions.

Additional Informa