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. All qualified applicants will receive consideration for employment regardless of race, color, religion, gender, gender identity or expression, sexual orientation, national origin, disability, age, or veteran status.
We are committed to building an inclusive culture and creating an environment where people can balance successful careers with their commitments and interests outside of work. Our flexible working practices support our belief that this balance brings long-lasting benefits for our business as well as our people. Some roles lend themselves to flexible options more than others, and if this is important to you, we are open to discussing agile working opportunities during the hiring process
At Experian, we are committed to find a solution whereby banks and other financial service providers could maximize data – specifically Open Banking data and transactional data sharing – to better inform decisions. That could mean credit application decisions or deciding which products best suit which customers throughout the lifetime. This is your opportunity to make a difference that is likely to benefit you as a customer sooner than you would expect!
We’re currently looking for a Data Scientist to join the R&D Team, part of our Analytics Center of Expertise, focused on rolling out Trusso, our cutting-edge ML based transaction categorization engine.
- Oversee and facilitate the training of a range of Machine Learning models,
- Build new ML-based solutions from scratch based on the newly available data
- Evaluate the quality of the trained models, using existing and also new measures
- Report on the current and historical state of our models’ performance, along with issues and progress,
- Incident resolution: Low accuracy troubleshooting, mis-categorisation issue analysis
- Maintenance and improvement of the existing model development pipeline
- Produce ad-hoc analysis/investigations on the modelling pipeline
- Document parts of the modelling/testing process
The successful candidate will have:
- A degree (BSc level) at a numerical discipline, such as Computer Science, Maths, Statistics, Physics, etc.
- Strong grasp of probability, statistical inference, optimization algorithms, linear algebra, and calculus.
- Understanding of how and when to apply key analytical approaches including regression analysis and machine learning
- Strong grasp of techniques for model cross-validation and penalizing unnecessary complexity, including regularization, in-sample vs out-of-sample.
- Ability to write near-production-level code in at least one general purpose programming language (e.g. Python, C++).
- Flexible and adaptable to learn and understand new technologies
- Demonstrable analytical and problem-solving abilities, coupled with an enquiring mind and the ability to learn quickly
- A keen eye for detail, good at spotting problems and quickly proposing solutions.
- Strong verbal and written communication skills (fluency in English)
Any of the following abilities and skills will be considered an advantage:
- Experience with natural language processing techniques and tools for the parsing of unstructured data.
- Experience with deep learning and other advanced modeling approaches to extract and automate insights from data.
- Fluency with statistical significance tests and Monte Carlo simulation.
- Experience with automation packages, such as Luigi and make
- Exposure to Linux.
- Personal Development – career pathway for professional growth supported by learning and development programs and unlimited access to online educational training courses, learning materials & books
- Work environment – excellent work conditions with friendly environment, recognized strong team spirit, and fun and quality recreation time
- Social benefit package – life insurance, food vouchers, additional health insurance, corporate discounts, Multisport card, and a Share options scheme
- Work-life balance – 25 days paid vacation and 3 additional paid days for participation in Social responsibility events
- Opportunity for Flexible working hours and Home Office
In order to stay safe and be responsible, we introduce a remote hiring process with online interviews for all candidates.