Data Scientist, Featurespace
Confirmed on the employer's own hiring board on Oct 27, 2026. First seen by Alion on Oct 26, 2026. Visa scores A on the Alion truth index.
About Us
Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid.
At Visa, you'll have the opportunity to create impact at scale - tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world.
Join Visa and do work that matters - to you, to your community, and to the world. Progress starts with you.
Job Description
What it's it all about:
At Featurespace, part of Visa, we build technology that helps financial institutions protect their customers from fraud and financial crime.
We’re looking for a Data Scientist to join our team, developing and managing the analytical capabilities at the heart of our fraud and risk prevention products.
The opportunity
This isn’t a role where you build a model and hand it over.
You’ll work across the full lifecycle of analytical products, from understanding customer problems and exploring data through to developing, deploying, monitoring and improving models in production.
You’ll combine strong data science with product thinking and operational ownership. You’ll see how your models perform with real customer data, understand the value they deliver, and help ensure they remain effective and well-managed as data, customer needs and fraud patterns change.
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.