MastercardAP Lead Data Scientist – Financial Crime
Mastercard is hiring an AP Lead Data Scientist – Financial Crime in Singapore, Singapore. Level rates it ; you can apply on Mastercard.
Level's read on this job
- On Level's scale, this data scientist role is Builds AI ●●●●: the job is building AI systems.
- Tools and skills the posting cites include scikit-learn, Python and Machine Learning Frameworks.
- Level classifies it as a lead role.
- Level lists 946 other open roles at Mastercard.
AI in this role
Build and maintain advanced machine learning models to detect fraud, scams, and money laundering.
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
AP Data Scientist - Financial CrimeLead Data Scientist – Financial CrimeWho is 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. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realize their greatest potential.
Our decency quotient, or 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.
Overview
In the Financial Crime Solutions (FCS) team at Mastercard, we build and deliver products and services powered by payment data to detect and prevent financial crime. We are an award winning team, combining advanced data science with deep payment expertise to help financial institutions combat fraud, scams, and money laundering by uncovering the criminal behaviours that drive them.
We are now building a new Centre of Excellence in Asia-Pacific, designed to act as a regional hub for the Cyber & FCS vertical. The Centre will drive scalable growth and robust delivery tailored to APAC priorities, and strengthen Mastercard’s leadership in anti scam and fraud prevention through thought leadership and industry engagement.
Role
As a Lead Data Scientist, you will be a core contributor to a newly established Centre of Excellence, building and maintaining data science models that help detect fraud, scams, and money laundering using payments data.
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 the regional team.
A key aspect of this role is building and sustaining local data science capabilities within the Centre of Excellence, ensuring we can effectively support customers across Asia Pacific while remaining closely aligned with global strategy and guidance. This includes acting as regional experts in the development, maintenance, and application of financial crime models, enabling timely, context aware support for our regional clients.
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 various technology stacks.
• 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.
• Act as a regional expert in building, maintaining, and evolving financial crime models, ensuring they remain effective, well understood, and appropriately governed.
• Support customers across Asia Pacific by providing data science guidance and services, informed by global frameworks while tailored to local market needs and constraints.
• Contribute to and support regional initiatives such as proof of value exercises and targeted solution deployments.
• Collaborate closely with global data science and product teams to align on methodology, research direction, and model strategy, while owning regional execution and contextualisation.
• Enable scalable customer support by strengthening local DS ownership, reducing dependency on global teams for region specific model understanding and application.
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, polars, 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 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.
• You had professional exposure delivering data science solutions, ideally within a regulated or payments adjacent environment and with an understanding of regional market, customer, and regulatory considerations.
• Comfortable operating in a federated model, balancing global standards and guidance with local ownership, execution, and customer engagement.
• Fluent in English
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:
• A strong academic foundation in a scientific or closely related discipline, such as data science, computer science, mathematics, statistics, physics, or engineering
• Streaming and real time data processing
• Modern machine learning approaches, including deep learning frameworks
• Network and graph based analysis to model complex behaviours.
• Development of custom data visualisations and analytical prototypes
Corporate Security Responsibility
Every person working for, or on behalf of, Mastercard is responsible for information security. All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and therefore, it is expected that the successful candidate for this position 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.
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.
How we rate this
AP Lead Data Scientist – Financial Crime at Mastercard rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Pay
- Typical data scientist job
- $168k
This posting doesn't list pay. Use the typical data scientist figure when pay comes up. See data scientist jobs
Questions you could be asked
- Tell me about a project where data science was part of your work. What did you do?
- Tell me about a project where financial crime was part of your work. What did you do?
- Tell me about a project where fraud detection was part of your work. What did you do?
- Tell me about a project where predictive modeling was part of your work. What did you do?
- Walk me through how you've used scikit-learn in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: Data Science, Financial Crime, Fraud Detection, Predictive Modeling, and scikit-learn. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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