Lead Data Scientist – Financial Crime Solutions
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 techniques with an intimate knowledge of payments data to aid financial institutions in their fight against money laundering and fraud. We craft bespoke algorithms that help our clients understand the underlying criminal behaviour that drives financial crime, empowering them to take action.
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.
- Conduct 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 for external clients.
- Be ready to learn new technologies 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, including privacy, security, regulation, code performance, and model accuracy.
Skills Required
- Write Python to a high standard and be familiar with standard data‑science libraries such as pandas, scikit‑learn, and networkx.
- Develop new algorithms in novel situations and demonstrate previous work to evidence this.
- Have a keen interest in modelling the behaviours exposed by payments data.
- Communicate technical matters to non‑tech colleagues and understand their perspectives.
- Explore new programming languages, technologies, and techniques enthusiastically.
- Maintain a can‑do attitude, be pragmatic where necessary, and enjoy working as part of a specialist team.
- Engage in constructive criticism and be comfortable with code reviews.
Desirable Experience
- Practical experience using streaming technologies, including platforms such as Kafka, online algorithms such as stochastic gradient descent, and fixed‑memory data structures such as 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 the server‑side microservices to support them.
Corporate Security Responsibility
All activities involving access to Mastercard assets, information, and networks carry an inherent risk. 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 accessed; report any suspected information‑security violation or breach; and complete all mandatory security training in accordance with Mastercard’s guidelines.
