Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Lead Data Scientist

Mastercard
London
4 days ago
Create job alert
Overview

Mastercard powers economies and empowers people in 200+ countries and territories worldwide. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. In Mastercard’s Financial Crime Solutions team, we build and deliver products and services powered by payments data to find and stop financial crime. We are an award-winning team combining data science with deep knowledge of payments data to aid financial institutions in their fight against money laundering and fraud.


Role summary

As a Data Scientist, you will join one of the first teams in the world looking at payments data in the UK and across the world. You will be product-focused, collaborating with engineering, operations data scientists, and the wider sales, consulting, and product teams. You will help build systems that expose money laundering and detect fraud, and work with clients to understand underlying behaviours used by criminals.


Responsibilities

  • Perform proof-of-concept projects, engage in product design and build prototypes.
  • Use the full range of data science techniques to develop new and novel algorithms to aid existing and new financial crime products.
  • Perform novel research to help us and our clients understand different criminal behaviours in payments data.
  • Turn derived insights into new products and services offered to external clients.
  • Learn new technologies as required and engage with legacy and future technology stacks, in the UK and internationally.
  • Write white papers, patents, and client-facing data visualisations.
  • Consider privacy, security, regulation, performance of code, and accuracy of models in your work.

Security and compliance

  • Every person working for, or on behalf of Mastercard is responsible for information security.
  • Abide by Mastercard's security policies and practices; ensure confidentiality and integrity of information accessed.
  • Report suspected information security violations or breaches.
  • Complete periodic mandatory security trainings in accordance with Mastercard's guidelines.

Qualifications and experience

  • Core skills: You can write Python to a high standard and are familiar with standard data science libraries (pandas, scikit-learn, networkx).
  • You are capable of developing new algorithms in novel situations and can demonstrate previous work.
  • You understand the data we work with and have an interest in modelling the behaviours it exposes.
  • You can communicate with non-technical colleagues about technical matters and consider others' perspectives.
  • You are excited to explore new programming languages, technologies, and techniques and have a can-do attitude.
  • You are open to peer review and constructive criticism.
  • You are comfortable working in a setting that often breaks new ground and are keen to explore new technologies and techniques.

    • Desirable experience:

      • Practical experience with streaming technologies and platforms (e.g., Kafka), online algorithms (e.g., stochastic gradient descent), and fixed-memory data structures (e.g., Bloom filters).
      • Experience with next-generation machine learning techniques and tools, including Deep Neural Networks and TensorFlow.
      • Exposure to Network Theory, especially social network analysis and graph diffusion analysis.
      • Ability to build custom data visualisations, prototype browser-based UX/UI, and server-side microservices to support them.





About Mastercard

Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Our decency quotient (DQ) drives our culture and everything we do inside and outside of our company. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all.


#J-18808-Ljbffr

Related Jobs

View all jobs

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist - Customer Development

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How to Write an AI CV that Beats ATS (UK examples)

Writing an AI CV for the UK market is about clarity, credibility, and alignment. Recruiters spend seconds scanning the top third of your CV, while Applicant Tracking Systems (ATS) check for relevant skills & recent impact. Your goal is to make both happy without gimmicks: plain structure, sharp evidence, and links that prove you can ship to production. This guide shows you exactly how to do that. You’ll get a clean CV anatomy, a phrase bank for measurable bullets, GitHub & portfolio tips, and three copy-ready UK examples (junior, mid, research). Paste the structure, replace the details, and tailor to each job ad.

AI Recruitment Trends 2025 (UK): What Job Seekers Must Know About Today’s Hiring Process

Summary: UK AI hiring has shifted from titles & puzzle rounds to skills, portfolios, evals, safety, governance & measurable business impact. This guide explains what’s changed, what to expect in interviews, and how to prepare—especially for LLM application, MLOps/platform, data science, AI product & safety roles. Who this is for: AI/ML engineers, LLM engineers, data scientists, MLOps/platform engineers, AI product managers, applied researchers & safety/governance specialists targeting roles in the UK.

Why AI Careers in the UK Are Becoming More Multidisciplinary

Artificial intelligence is no longer a single-discipline pursuit. In the UK, employers increasingly want talent that can code and communicate, model and manage risk, experiment and empathise. That shift is reshaping job descriptions, training pathways & career progression. AI is touching regulated sectors, sensitive user journeys & public services — so the work now sits at the crossroads of computer science, law, ethics, psychology, linguistics & design. This isn’t a buzzword-driven change. It’s happening because real systems are deployed in the wild where people have rights, needs, habits & constraints. As models move from lab demos to products that diagnose, advise, detect fraud, personalise education or generate media, teams must align performance with accountability, safety & usability. The UK’s maturing AI ecosystem — from startups to FTSE 100s, consultancies, the public sector & universities — is responding by hiring multidisciplinary teams who can anticipate social impact as confidently as they ship features. Below, we unpack the forces behind this change, spotlight five disciplines now fused with AI roles, show what it means for UK job-seekers & employers, and map practical steps to future-proof your CV.