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- Tasks: Design and deploy machine learning models to combat financial crime and fraud.
- Company: Join NTT DATA, a global leader in technology services and innovation.
- Benefits: Flexible work options, competitive salary, and opportunities for professional growth.
- Other info: Be part of a diverse team committed to responsible innovation.
- Why this job: Make a real impact in preventing financial crime using advanced analytics.
- Qualifications: 5+ years in Data Science with strong Python and PySpark skills.
The predicted salary is between 43200 - 72000 £ per year.
NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now. We are looking for an experienced Senior Data Scientist with strong expertise in Python, PySpark, and advanced analytics, along with a solid understanding of Financial Crime, Fraud Monitoring, and AML concepts. The ideal candidate will work on large-scale data to build, enhance, and optimize analytical and machine learning models used for fraud detection and financial crime prevention.
Key Responsibilities
- Design, develop, and deploy data science and machine learning models for fraud detection, transaction monitoring, and financial crime use cases
- Analyze large, complex datasets using Python and PySpark in distributed data environments
- Build end-to-end analytics pipelines including data ingestion, feature engineering, model training, and validation
- Apply statistical analysis, ML techniques, and pattern recognition to identify suspicious behaviours and emerging fraud typologies
- Collaborate with business, compliance, and technology teams to translate financial crime requirements into analytical solutions
- Monitor model performance, perform tuning, and ensure model stability and regulatory alignment
- Document models, methodologies, and assumptions for internal governance and audit requirements
- Stay updated on financial crime trends, fraud patterns, and regulatory expectations
Required Skills & Qualifications
- 5+ years of experience in Data Science, Analytics, or a related role
- Strong proficiency in Python (NumPy, Pandas, Scikit-learn, etc.)
- Hands-on experience with PySpark / Spark for large-scale data processing
- Solid understanding of Financial Crime domains including: Fraud Monitoring, Transaction Monitoring, AML / CTF concepts, Customer risk and suspicious activity patterns
- Experience building and validating machine learning models (supervised & unsupervised)
- Strong knowledge of data preprocessing, feature engineering, and model evaluation
- Ability to communicate complex analytical findings to non-technical stakeholders
NTT DATA is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.
Senior Data Scientist - Financial Crime employer: NTT Data Americas, Inc.
Contact Detail:
NTT Data Americas, Inc. Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Scientist - Financial Crime
✨Tip Number 1
Network like a pro! Reach out to your connections in the data science and financial crime sectors. Attend meetups, webinars, or even online forums to get your name out there. You never know who might have a lead on that perfect Senior Data Scientist role!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving Python, PySpark, and machine learning models for fraud detection. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your knowledge of financial crime trends and regulatory expectations. Be ready to discuss how you've applied your analytical skills in real-world scenarios. Confidence is key, so practice makes perfect!
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining NTT DATA and being part of our innovative team.
We think you need these skills to ace Senior Data Scientist - Financial Crime
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Python, PySpark, and financial crime concepts. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about data science and financial crime prevention. Let us know what excites you about this role and how you can contribute to our team.
Showcase Your Analytical Skills: In your application, include examples of how you've used advanced analytics and machine learning in past roles. We love seeing real-world applications of your skills, especially in fraud detection and transaction monitoring.
Apply Through Our Website: We encourage you to apply directly through our website for the best chance of getting noticed. It’s quick and easy, and we can’t wait to see your application come through!
How to prepare for a job interview at NTT Data Americas, Inc.
✨Know Your Tech Inside Out
Make sure you’re well-versed in Python, PySpark, and the tools mentioned in the job description. Brush up on libraries like NumPy and Pandas, and be ready to discuss your experience with large-scale data processing. They’ll want to see that you can not only use these tools but also understand their underlying principles.
✨Understand Financial Crime Concepts
Dive deep into financial crime, fraud monitoring, and AML concepts. Be prepared to discuss specific cases or trends you've encountered in your previous roles. Showing that you can connect your technical skills to real-world applications in financial crime will set you apart.
✨Prepare for Technical Questions
Expect to face technical questions that test your knowledge of machine learning models and analytics pipelines. Practice explaining your thought process when building and validating models, as well as how you handle data preprocessing and feature engineering. Clear communication is key!
✨Showcase Collaboration Skills
Since the role involves working with various teams, be ready to share examples of how you’ve collaborated with business, compliance, and tech teams in the past. Highlight your ability to translate complex analytical findings into actionable insights for non-technical stakeholders.