Vice President - Data Science / Applied AI ML
On-siteHyderabad, Telangana, India
Job Summary
Lead CCOR Data Science initiatives to design, deploy, and operate production-grade GenAI/AI/ML solutions across risk and compliance use cases, focusing on measurable risk mitigation and regulatory alignment. Drive research in supervised/unsupervised learning, graph analytics, and anomaly detection to improve true-positive rates and investigator productivity. Own the end-to-end model lifecycle, from problem framing and feature engineering to validation, bias checks, and retraining, while maintaining rigorous model risk management practices. Build MLOps pipelines, model registries, and automated monitoring for scalable operations. Partner with Risk, Compliance, Investigations, and Technology to translate regulatory expectations into defensible ML controls. Deploy explainability techniques like SHAP and LIME to enhance decisioning and usability. Maintain a pragmatic approach to GenAI while prioritizing classical ML for core detection efficacy. Requires a Master's or PhD in a quantitative discipline and 7+ years of hands-on GenAI/AI/ML experience in Financial Crime Compliance, AML, or fraud.
Required Qualifications
- Master's or PhD in a quantitative discipline (Computer Science, Statistics, Mathematics, Economics, Operations Research, or related)
- Minimum of 7 years of hands-on Gen AI/ AI/ ML experience within Financial Crime Compliance, AML, sanctions, fraud, or related risk & compliance domains; deep knowledge of regulatory & control expectations
- Proven leadership delivering production AI/ML for compliance & risk, including transaction monitoring models, risk scoring, anomaly detection, network/graph analytics, and/or investigator triage/prioritization at enterprise scale
- Advanced Python skills; strong experience with AI/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, benchmarking/challenger models, back testing, stability and drift analysis, champion/challenger governance, and 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
- Ability to mentor junior team members through code reviews, pairing, and technical guidance
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