Key Responsibilities:
- Develop and optimize advanced machine learning models and algorithms for fraud detection and AML applications.
- Mentor and guide junior data scientists and analysts, fostering a collaborative and high-performance team environment.
- Leverage cloud platforms (AWS, Azure, Google Cloud) to implement scalable AI/ML solutions.
- Contribute to the design and implementation of core algorithms, mathematical models, and data-driven solutions.
- Explore and apply emerging technologies such as Generative AI to enhance fraud detection capabilities.
- Collaborate with product managers, engineers, and other stakeholders to translate business requirements into robust technical solutions.
- Perform statistical analysis, data mining, and visualization using tools like Python or R.
- Drive innovation by researching and integrating the latest advancements in data science and machine learning.
- Support the team in building user behavior models, leveraging Bayesian statistics, and exploring advanced techniques like social network analysis.
Skills and Experience Required:
- Educational Background:
- Master’s or Ph.D. in Statistics, Applied Mathematics, Data Science, Computer Science, Electrical Engineering, or a related quantitative field.
- Professional Experience:
- 2 –4 years of experience in algorithm development, statistical analysis, and machine learning.
- Hands-on experience in applying advanced machine learning techniques to real-world datasets in financial fraud prevention, AML, or similar domains.
- Technical Expertise:
- Proficiency in Python for statistical analysis, data modeling, and visualization.
- Experience with cloud technologies and platforms (AWS, Azure, or Google Cloud).
- Solid understanding of databases and SQL (e.g., MySQL).
- Exposure to generative AI techniques and their applications in data science.
- Soft Skills and Teamwork:
- Strong mentoring and leadership skills, with a proven ability to guide and develop junior team members.
- Excellent problem-solving skills with a pragmatic approach to balancing theory and practical application.
- Effective communication skills to collaborate across teams and present complex ideas to stakeholders.
- Resourceful, adaptable, and passionate about financial crime prevention technologies.
Preferred Qualifications:
- Knowledge of user behavior modeling and Bayesian statistics.
- Experience in natural language processing (NLP).
- Familiarity with tools and libraries for generative AI (e.g., Transformer models).
- Understanding of the financial crime prevention domain and its associated challenges.
Reporting into: Tech Manager
Individual Contributor