Senior Data Scientist, Payments Foundation Models at Visa

Cambridge, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

15 Feb, 26

Salary

0.0

Posted On

17 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Science, Machine Learning, Statistical Modeling, Data Engineering, Deep Learning, Python, R, SQL, Problem Solving, Communication, Mentoring, Fraud Detection, Risk Analytics, Model Risk Management, PyTorch, Artificial Neural Networks

Industry

IT Services and IT Consulting

Description

Company Description Visa is a world leader in payments and technology, with over 259 billion payments transactions flowing safely between consumers, merchants, financial institutions, and government entities in more than 200 countries and territories each year. Our mission is to connect the world through the most innovative, convenient, reliable, and secure payments network, enabling individuals, businesses, and economies to thrive while driven by a common purpose – to uplift everyone, everywhere by being the best way to pay and be paid. Make an impact with a purpose-driven industry leader. Join us today and experience Life at Visa. Job Description What it's all about - The Payments Foundation Models team is a new, high-impact initiative within Visa’s Data Science organisation. Based in Cambridge, UK, and working closely with global Visa engineering and product teams, the group’s mission is to build the next generation of payments-focused foundation AI models. These models will power a range of premium Risk and Identity Solutions (RaIS) products, such as fraud scores, with the goal of generating more than 100M dollars in new revenue by FY2030 and may be extended into other domains such as credit risk modelling or agentic commerce personalization. In your role as Senior Data Scientist – Payments Foundation Models you will help us achieve our goals and deliver success on behalf of our customers by: Developing, training, evaluating, documenting and disseminating Payments Foundation Models for use in data science and AI projects across Visa. Collaborating across the organization with engineering, data science, research, product and commercial teams to improve the quality, adoption and real-world impact of our models. This is a hands-on technical role in the Individual Contributor track at the Consultant or Manager level, with scope to influence data science standards and practices while working on high-impact, Visa-scale systems. Responsibilities: We hire people with a willingness to adapt to a variable role, so along with the key responsibilities below, we ask for ownership of any other duties as required. Collaborating with Product Managers & other members of the team to align on the highest value items to work on Coordinating work across multiple teams & when needed, taking on additional “tech lead” responsibilities for driving initiatives to completion. Identifying risks and testing assumptions before development End-to-end processing and modelling of large data sets Training deep learning models utilizing self-supervised training, supervised fine-tuning or adaptation approaches. Ensuring new deep learning models successfully navigate model risk management processes, ensuring high quality documentation exists alongside analytics products (reports, presentations, visualizations) Leading the deployment and maintenance of statistical models and algorithms Collaborating with data engineers to identify and implement improvements to tooling. Enabling both technical and non-technical colleagues by effectively communicating insights learnt during data science work Evangelizing on the benefits of deep learning models within Visa Improving team processes and providing input to future team strategy Mentoring more junior members of the team as well as managing and prioritising their workload to ensure high-quality output. Developing a solid understanding of the fraud and financial crime industries This is a hybrid position. Expectation of days in the office will be confirmed by your Hiring Manager. Qualifications What we'd like from you - Preferred Qualifications: Advanced degree in Data Science, Computer Science, Physics, Mathematics, or related field. Strong background in machine learning, statistical modeling, and data engineering. Enthusiasm for bringing cutting-edge deep learning models into production at Visa-wide scales. Practical experience managing large-scale datasets and conducting end-to-end analytics projects. Proficiency with programming languages such as Python or R, and familiarity with SQL and big data tools. Technical and analytical skills with the ability to pick up new technologies and concepts quickly. Problem solving skills (especially in data-centric applications). Strong, clear, concise written and verbal communication skills. Ability to manage and prioritise personal workload Excellent communication skills for technical and non-technical stakeholders. Proven ability to lead cross-functional projects and mentor junior team members. Experience with fraud detection, risk analytics, or financial crime prevention. Experience developing models within a model risk management framework. Experience with version control software and workflows (e.g. git) Experience with PyTorch or another deep learning framework Familiarity with the training and serving of artificial neural networks Subject matter expertise in the banking and payments industry. Additional Information 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.

Responsibilities

As a Senior Data Scientist, you will develop and disseminate Payments Foundation Models for data science and AI projects at Visa. You will collaborate with various teams to enhance model quality and impact while leading the deployment and maintenance of statistical models.