At a Glance
- Tasks: Lead innovative projects to combat financial crime using advanced data science techniques.
- Company: Join Mastercard's award-winning Financial Crime Solutions team.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on learning and innovation.
- Why this job: Make a real difference in the fight against fraud and money laundering.
- Qualifications: Strong Python skills and experience with data science libraries required.
The predicted salary is between 60000 - 80000 £ per year.
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.
Lead Data Scientist in London employer: Mastercard
Mastercard is an exceptional employer, particularly for those in the Lead Data Scientist role within our Financial Crime Solutions team. We foster a collaborative and innovative work culture that encourages continuous learning and professional growth, offering employees the chance to engage with cutting-edge technologies and contribute to meaningful projects that combat financial crime. Located in the UK, our team not only enjoys competitive benefits but also the unique opportunity to make a significant impact on global financial security.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Data Scientist in London
✨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!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Lead Data Scientist at 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 Lead Data Scientist in London
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.
