Lead Data Scientist

Our Purpose

Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. 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.

Title and Summary

Lead Data Scientist

In the Financial Crime Solutions team at Mastercard, we build and deliver products and services powered by payments data to find and stop financial crime. We’re an award winning team with a proven track record of combining data science technique with an intimate knowledge of payments data to aid Financial Institutions in their fight against money laundering and fraud. Headquartered in The City of London, and operating globally, we craft bespoke algorithms that help our clients gain an understanding of the underlying criminal behaviour that drives financial crime, empowering them to take action.

Role

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. In the research discipline you will help build systems that expose money laundering and detect fraud as well as work with the other data scientists and clients to understand the underlying behaviours employed by criminals. You will be product focused, working in close collaboration with our engineering and operations data scientists as well as the wider sales, consulting, and product teams. 

In this position, you will:

- Perform proof-of-concept projects, engage in product design and build prototypes. 
- Use the full range of data science based techniques to develop new and novel algorithms to aid existing and new financial crime products. 
- Be able to perform novel research to help us and our clients understand the different criminal behaviours in payments data. 
- Think about how derived insights can be turned into new products and services we can offer to external clients.  
- Be ready to 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 the full impact of your work. This means considering privacy, security, and regulation, as well as the performance of your code and the accuracy of your models. 

Skills Required

Your passion is focused on the design of algorithms to solve real, pressing problems using data. You will have an interest in the financial services industry and want to tackle financial crime in the wider economy. You are excited by building products for clients and are keen to engage in the design processes this involves. Specifically: 

- You can write Python to a high standard and are familiar with the standard data science libraries such as pandas, scikit-learn and networkx. 
- You are capable of developing new algorithms in novel situations and can demonstrate previous work to evidence this. 
- You are keen to understand the data we work with and have a keen interest in how to model the behaviours it exposes. 
- You are able to communicate with non-tech colleagues about technical matters, and you are comfortable putting yourself in other people's shoes. 
- You are happy and excited to explore new programming languages, technologies, and techniques. 
- You have a can-do attitude, can be pragmatic where necessary, and are excited to work as part of a specialist team. You can engage in constructive criticism and aren't afraid to have your code reviewed. 

As we are often breaking new ground, both for Mastercard and more widely in our sector, we strongly encourage exploring new technologies and techniques. Some of the following experience is therefore desirable: 

- Practical experience using streaming technologies, including streaming platforms (e.g. Kafka), online algorithms (e.g. stochastic gradient descent), and fixed-memory data structures (e.g. Bloom Filters). 
- Experience using 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 the server side microservices to support them. 

Corporate Security Responsibility

All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:

• Abide by Mastercard’s security policies and practices;

• Ensure the confidentiality and integrity of the information being accessed;

• Report any suspected information security violation or breach, and

• Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.