As a Junior Data Scientist, you will join one of the first teams in the world looking at account to account for payment data across the world. Within the research team, you will help build systems that expose money laundering and detect frauds and scams, as well as help Mastercard and their clients, understand the underlying behaviors employed by criminals. You will be product-focused, working in close collaboration with their engineering and operations data scientists as well as the wider sales, consulting, and product teams.
All About You
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 standard data science libraries such as pandas, scikit-learn and networks.
You’re capable of developing new algorithms in novel situations and can demonstrate previous work to evidence this.
You’re keen to understand the data we work with and are interested in how to model the behavior it exposes.
You’re able to communicate with non-technical colleagues about technical matters, and you’re comfortable putting yourself in other people’s shoes.
You’re proficient in SQL and git and can work in the bash shell. You’re 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.
Some Of The Following Experience Is Therefore Desirable
As we are often breaking new ground, both for Mastercard and more widely in their sector, they strongly encourage exploring new technologies and techniques.
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.
Exposure to Network Theory, especially social network analysis and graph diffusion analysis.
Mastercard is an inclusive Equal Employment Opportunity employer that considers applicants without regard to gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law.
If you require accommodations or assistance to complete the online application process, please contact reasonable.accommodation@mastercard.com and identify the type of accommodation or assistance you are requesting. Do not include any medical or health information in this email. The Reasonable Accommodations team will respond to your email promptly.
