Data Scientist- AML Risk Analytics

About Us:

Solytics Partners is a Global Analytics firm, recognized with multiple industry awards for innovation and excellence. Our team comprises experts with deep domain knowledge in risk, analytics, AI/ML, AML/FCC, and fraud. By converging this expertise with cutting-edge technologies like AI, Machine Learning, Generative AI, and Large Language Models (LLMs), we deliver powerful automated platforms and incisive point solutions.

Our offerings enable clients to streamline and future-proof their risk, AML, and analytics processes, comply seamlessly with global regulations, and safeguard financial systems. Whether its solving complex challenges or driving operational efficiency, Solytics Partners is committed to empowering organizations with transformative tools to stay ahead in an evolving regulatory landscape.

Job Summary:

We are seeking an experienced AML Model Development & Validation Analyst to support the design, development, validation, and continuous enhancement of Transaction Monitoring models. The ideal candidate will have hands-on experience in model development or validation, with strong expertise in scenario tuning, threshold optimization, customer segmentation, and model calibration. This role involves working closely with Compliance, Financial Crime, Risk, and Technology teams to ensure transaction monitoring models remain effective, risk-sensitive, and compliant with regulatory expectations while minimizing false positives.

Key Responsibilities:

• Develop, validate, and enhance AML Transaction Monitoring models and detection scenarios across various financial crime typologies.
• Perform customer and transaction segmentation to improve model performance and risk coverage.
• Design, calibrate, and optimize monitoring scenarios by reviewing thresholds, parameters, and detection logic.
• Conduct scenario tuning and threshold optimization using historical alert data, customer behavior, and transactional analysis to improve detection efficiency.
• Execute model validation activities, including conceptual reviews, performance assessments, back-testing, benchmarking, and outcome analysis.
• Analyze model performance metrics such as alert volumes, false positive rates, detection effectiveness, and model stability, and recommend improvements.
• Assess transaction monitoring models for alignment with regulatory requirements, internal policies, and financial crime risk appetite.
• Perform data analysis on large transactional datasets to identify emerging money laundering patterns and validate model effectiveness.
• Collaborate with Compliance, Financial Crime, Data Science, Technology, and Business stakeholders during model development, implementation, and validation initiatives.
• Support periodic model reviews, governance activities, model documentation, validation reports, and audit or regulatory examinations.

Key Requirements:

• 3–8 years of experience in Anti-Money Laundering (AML), Financial Crime Compliance, or Model Risk Management within the banking or financial services industry.
• Hands-on experience in AML Model Development, Model Validation, Model Calibration, or Transaction Monitoring Optimization.
• Strong understanding of Transaction Monitoring models, customer risk segmentation, scenario development, scenario tuning, threshold optimization, and alert generation methodologies.
• Experience validating or developing AML detection scenarios for various money laundering typologies, including structuring, high-velocity transactions, unusual account activity, sanctions evasion, or other financial crime risks.
• Good understanding of model validation techniques, model performance assessment, back-testing, sensitivity analysis, and model calibration.