At a Glance
- Tasks: Develop and deploy machine learning solutions to combat financial crime in payments.
- Company: Join Mastercard, a leader in global payments and financial security.
- Benefits: Attractive salary, health benefits, and opportunities for professional growth.
- Other info: Be part of a dynamic team focused on innovative data-driven solutions.
- Why this job: Make a real difference by preventing fraud and protecting customers worldwide.
- Qualifications: Strong Python skills and experience with large datasets required.
The predicted salary is between 63000 - 77000 £ per year.
Mastercard is seeking a Senior Data Scientist to join the Financial Crime Solutions Data Science team.
You will develop, deploy, and support machine learning solutions that prevent financial crime across the global payments ecosystem, with a focus on A2A fraud, scam, and mule detection.
The role requires strong Python skills, experience with large-scale datasets, and the ability to deliver measurable value to customers through data-driven solutions.
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Senior Fraud ML Scientist — Payments & Financial Crime employer: MasterCard
Mastercard is an exceptional employer that fosters a culture of innovation and collaboration, making it an ideal place for a Director of Software Engineering. With a strong emphasis on employee growth, you will have access to cutting-edge tools and resources, as well as opportunities to mentor and lead talented teams across diverse geographies. The company's commitment to data-driven decision-making and AI-assisted development ensures that you will be at the forefront of technological advancements in a dynamic and supportive environment.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Fraud ML Scientist — Payments & Financial Crime
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like MasterCard!
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like MasterCard.
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We think you need these skills to ace Senior Fraud ML Scientist — Payments & Financial Crime
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at MasterCard, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at MasterCard. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at MasterCard
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at MasterCard!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
