Senior Associate - Applied AI ML / Data Science
On-site · Bengaluru, Karnataka, India
Job Summary
Lead and scale ML solutions for Financial Crime Data Science in AML; design, deploy, and operate production-grade ML solutions across AML transaction monitoring use cases with focus on reducing false positives and regulatory alignment. Drive research in supervised/unsupervised/semi-supervised learning, graph/network analytics, and anomaly detection; own end-to-end model lifecycle, from problem framing to retraining. Build robust MLOps pipelines and governance artifacts; mentor and grow a high-performing team; collaborate with FCC, Investigations, Operations, and Technology to translate red flags into defensible ML controls; deploy interpretable ML with explainability techniques and human-in-the-loop feedback to improve precision and usability; prioritize regulatory review readiness and model risk management throughout the lifecycle; explore GenAI/LLMs as supportive tools while emphasizing classical ML for core detection efficacy; location-based to Bengaluru, India.
Required Qualifications
- Master’s or PhD in Computer Science, Statistics, Mathematics, Economics, Operations Research, or related
- Minimum of 6 years hands-on ML experience
- 3+ years in Financial Crime Compliance, AML, sanctions, fraud, or related risk domains
- Deep knowledge of regulatory expectations (e.g., AML program requirements, sanctions controls, model governance)
- Proven leadership delivering production ML for financial crime (transaction monitoring models, risk scoring, anomaly detection, network/graph analytics)
- Advanced Python skills; strong experience with ML frameworks
- Expertise in supervised learning, anomaly detection, semi‐supervised learning, clustering, feature stores, and calibration/threshold optimization
- Familiarity with imbalanced learning and cost-sensitive evaluation
- Demonstrated experience in model risk management (documentation, validation, backtesting, drift analysis, explainability suitable for regulatory review)
- Excellent communication skills to translate and explain complex models with clear reason codes, and influence cross-functional stakeholders and senior leadership
- People leadership: recruiting, coaching, performance management, and fostering an inclusive, high-accountability culture
Desired Qualifications
- Master’s or PhD in Computer Science, Statistics, Mathematics, Economics, Operations Research, or related
- Minimum of 6 years hands-on ML experience
- 3+ years in Financial Crime Compliance, AML, sanctions, fraud, or related risk domains
- Deep knowledge of regulatory expectations (e.g., AML program requirements, sanctions controls, model governance)
- Proven leadership delivering production ML for financial crime (transaction monitoring models, risk scoring, anomaly detection, network/graph analytics)
- Advanced Python skills; strong experience with ML frameworks
- Expertise in supervised learning, anomaly detection, semi‐supervised learning, clustering, feature stores, and calibration/threshold optimization
- Familiarity with imbalanced learning and cost-sensitive evaluation
- Demonstrated experience in model risk management (documentation, validation, backtesting, drift analysis, explainability for regulatory review)
- Excellent communication skills to translate complex models for cross-functional stakeholders
- People leadership: recruiting, coaching, performance management, inclusive culture
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