We are the leading global information services company, providing data and analytical tools to our clients around the world. We help businesses to manage credit risk, prevent fraud, target marketing offers and automate decision making. We also help people to check their credit report and credit score and protect against identity theft. We employ approximately 17,000 people in 37 countries and our corporate headquarters are in Dublin, Ireland, with operational headquarters in Nottingham, UK; California, US; and São Paulo, Brazil. Experian is committed to creating a diverse environment and is proud to be an equal opportunity employer.

A Data Scientist extracts insights from raw data and uses them to solve a problem. Data scientists are in high demand as they can help companies make sense of the ever-growing amount of data available. A career in data science can be very rewarding as there are many opportunities for growth and development. The work is interesting, challenging, and intellectually stimulating. Here is a list of the top 10 career prospects in Data Science. Whether you're looking to start a career in data science or want to upgrade your existing skills, you'll need to be prepared to work with massive amounts of data. The demand for data analysts is growing rapidly. While the field has been around for a while, the latest trends and developments show that it is still very much alive and kicking.

Location: (Remote) Offices in London and Nottingham but will consider remote. Liberis is transforming business finance. We use the latest technology to make it simple and flexible for small businesses to secure funding. Since our inception we have helped more than 15,000 small businesses access over £390 million in funding. We believe that small businesses are the economic lifeblood of local communities, yet they continue to be underserved by traditional banks and financial services organisations.

Roles and responsibilities: Data modeller designs, implements, and documents data architect and data modeling solutions that include use of dimensional, relational, and NoSQL databases. The solutions support enterprise information management, business intelligence, machine learning, data science, and other business-related fields. The responsibilities of the data modeller include implementing business and IT data requirements through new data strategies and designs, work with business application teams, define data modeling and design standards, tools, and practices for enterprise models, identifying the architecture and infrastructure, hands-on designing and modeling, working independently to address project requirements and challenges to reduce project risks.