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- Tasks: Design and deploy models to combat fraud and financial crime using Python and PySpark.
- Company: Leading tech services firm in Greater London with a focus on innovation.
- Benefits: Attractive salary, flexible working options, and opportunities for professional growth.
- Why this job: Make a real difference in fighting financial crime while advancing your data science career.
- Qualifications: 5+ years of experience in data science and strong knowledge of financial crime.
- Other info: Collaborative team environment with exciting challenges and career advancement.
The predicted salary is between 43200 - 72000 £ per year.
A leading technology services firm in Greater London is seeking a Senior Data Scientist specializing in Python and PySpark. The role involves designing and deploying models for fraud detection and financial crime prevention, analyzing complex datasets, and collaborating with teams to translate requirements into analytical solutions.
Candidates should have over 5 years of relevant experience and a strong understanding of financial crime domains, including fraud monitoring and AML concepts.
Senior Data Scientist - Financial Crime & Fraud ML employer: NTT DATA
Contact Detail:
NTT DATA Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Scientist - Financial Crime & Fraud ML
✨Tip Number 1
Network like a pro! Reach out to professionals in the financial crime and fraud detection space. Attend meetups or webinars, and don’t be shy about asking for informational interviews. We all know that sometimes it’s not just what you know, but who you know!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects in Python and PySpark. Include any models you've designed for fraud detection or financial crime prevention. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for those interviews! Brush up on your knowledge of AML concepts and fraud monitoring techniques. We recommend practising common data science interview questions and even doing mock interviews with friends or mentors to build your confidence.
✨Tip Number 4
Apply through our website! We make it super easy for you to find roles that match your skills. Plus, applying directly shows your enthusiasm and commitment to joining our team. Don’t miss out on the chance to land that dream job!
We think you need these skills to ace Senior Data Scientist - Financial Crime & Fraud ML
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Python and PySpark, as well as your understanding of financial crime domains. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about fraud detection and financial crime prevention. We love seeing candidates who can connect their personal motivations to our mission.
Showcase Your Analytical Skills: In your application, include examples of how you've tackled complex datasets in the past. We’re looking for someone who can translate requirements into analytical solutions, so share any relevant experiences that demonstrate this ability.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you’re keen on joining our team!
How to prepare for a job interview at NTT DATA
✨Know Your Tech Inside Out
Make sure you’re well-versed in Python and PySpark, as these are crucial for the role. Brush up on your coding skills and be ready to discuss specific projects where you've used these technologies to tackle fraud detection or financial crime.
✨Understand Financial Crime Concepts
Familiarise yourself with key concepts like fraud monitoring and AML (Anti-Money Laundering). Be prepared to discuss how these concepts apply to your previous work and how you can leverage them in the new role.
✨Prepare for Data Challenges
Expect to face some complex datasets during the interview. Practice explaining your thought process when analysing data and be ready to showcase your problem-solving skills through real-life examples.
✨Collaborate and Communicate
Since collaboration is key in this role, think of examples where you’ve worked with teams to translate requirements into analytical solutions. Highlight your communication skills and how you ensure everyone is on the same page.