You won’t be developing your expertise in isolation. You’ll join a group of other Data Scientists in Cambridge focused on fraud and risk, working alongside colleagues with significant experience in the field. It’s a close-knit and collaborative team, with lots of opportunities for informal learning, mentoring and exchanging ideas with people tackling similar problems.
What you'll do
Develop ML models using rich and complex transactional datasets.
Work with customers, product managers, engineers and delivery teams to turn real-world problems into effective fraud and risk prevention products.
Take models from discovery and experimentation through to production.
Monitor deployed models, investigate changes in performance and identify opportunities for improvement.
Retrain, enhance and safely release models as data, customer needs and fraud patterns evolve.
Measure and communicate the impact of our fraud and risk prevention products.
Contribute to model governance, documentation and robust production processes.
Share knowledge, mentor colleagues and help improve how the team works.
This is a hybrid position. Hybrid employees can alternate time between both remote and office. Employees in hybrid roles are expected to work from the office 3 set days a week (Tuesday - Thursday), with a general guidepost of being in the office 50% or more of the time based on business needs.
Qualifications
What we'd like from you
Experience implementing machine-learning models or analytical algorithms in software.
Experience using Python, Java or another major programming language for data analysis or machine learning.
Experience using SQL to analyse data.
Practical experience working with large, diverse datasets.
Strong analytical and problem-solving skills, particularly for data-centric problems.
The ability to pick up new technologies and concepts quickly.
Experience collaborating with multiple stakeholders on technical projects.
Clear and concise written and verbal communication.
The ability to manage and prioritise your own work.
A degree in a scientific or numerate discipline, or equivalent professional experience.
Visa is an EEO Employer
Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law.
