Overview
Financial Crimes Data Scientist specializing in the development, deployment, and optimization of in-house AML and fraud detection models. Leverages machine learning, network analytics, and large-scale financial data to identify emerging threats, enhance detection effectiveness, reduce false positives, and support regulatory compliance across financial crime risk programs.
Responsibilities
A Financial Crimes Data Scientist develops, validates, and deploys advanced analytics and machine learning solutions to detect, prevent, and investigate financial crimes, including money laundering, fraud, sanctions violations, and terrorist financing.
Key Responsibilities
- Design, build, validate, and deploy in-house detection models for AML, fraud, and other financial crime risks.
- Develop risk-scoring, anomaly detection, predictive, and network analytics models using internal and external data sources.
- Create and optimize transaction monitoring scenarios and machine learning-driven alert generation frameworks.
- Implement model deployment pipelines and production monitoring to ensure effectiveness and regulatory compliance.
- Conduct feature engineering, model tuning, and performance assessment to improve precision, recall, and operational efficiency.
- Partner with Compliance, BSA/AML, Fraud, Investigations, and Technology teams to translate emerging risks into scalable detection strategies.
- Reduce false positives while increasing the identification of high-risk activity and suspicious behavior.
- Support model governance, validation, documentation, and regulatory examinations.
- Perform other duties as assigned.
Core Expertise
- Financial Crime Risk Management
- AML/BSA Compliance
- Fraud Analytics
- Machine Learning & AI
- Graph and Network Analytics
- Entity Resolution
- Transaction Monitoring
- Model Development & Deployment
- Python, SQL, Spark, Databricks
- Cloud Analytics Platforms (Azure/AWS)
Qualifications
- Programming Skills 6 knowledge of statistical programming languages like R,Python, and database query languages like SQL, Hive, Pig is desirable. Familiarity with Scala, Java, or C++ is an added advantage.
- Statistics 6 Good applied statistical skills, including knowledge of statistical tests, behavior clustering, distributions, regression, maximum likelihood estimators, etc. Proficiency in statistics is essential for data-driven companies.
- Strong Math Skills (Multivariable Calculus and Linear Algebra) - understanding the fundamentals of Multivariable Calculus and Linear Algebra is important as they form the basis of a lot of predictive performance or algorithm optimization techniques.
- Data Wrangling 6 proficiency in handling imperfections in data is an important aspect of a data scientist job description.
- Experience with Data Visualization Tools like Tableau and Power BI that help to visually encode data
- Network/Link Analytics
- Excellent Communication Skills 6 it is incredibly important to describe findings to a technical and non-technical audience.
- Strong Software Engineering Background
- Hands-on experience with data science tools
- Problem-solving aptitude
- Analytical mind and great business sense
CompensationThe base pay range for this position is USD $100,000.00/Yr. - USD $200,000.00/Yr. Exact offers will be determined based on job-related knowledge, skills, experience, and location.
