Data Scientist - Financial Crimes

Responsibilities

• Develop, implement, and optimize machine learning models to detect money laundering, fraud, and other financial crimes, leveraging techniques such as anomaly detection, clustering, and predictive modeling.
• Analyze large volumes of transactional and user data to identify suspicious patterns or behaviors indicative of financial crimes.
• Perform data analysis to support transaction monitoring, sanction, KYC and high-risk investigation teams, including performing ad-hoc data analysis, that assist Financial Crime Compliance leadership in decision-making and strategic planning processes.
• Design and implement data visualization dashboards and reporting tools to communicate insights and findings from financial crime detection efforts.
• Collaborate with internal stakeholders to design, develop, validate, and implement AML scenarios, and conduct threshold tuning to optimize performance.
• Document and manage projects related to threshold tuning, scenario design, and detection methodologies.
• Collaborate with cross-functional teams, including product, engineering, and operations to integrate machine learning solutions into existing systems and processes.
• Perform special projects, and additional duties and responsibilities as required.

Qualifications

Minimum Qualifications:

• Master's degree in Statistics, Mathematics, Finance, Computer Science Engineering or a similar Quantitative field, or equivalent practical experience
• 3+ years of experience in data analysis, statistical modeling, machine learning, with a focus on financial crime compliance or related domains.
• Proficiency in programming languages Python, HQL, Neo4J, Spark as well as experience with machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Experience with data visualization tools and techniques for creating interactive dashboards and reports (e.g., Tableau, Power BI, matplotlib).
• Strong analytical skills and the ability to work with large datasets to extract actionable insights.
• Demonstrate strong critical thinking skills, with the ability