Senior Associate - Data Science / Applied AI ML
On-siteHyderabad, Telangana, India
Job Summary
Deliver production AI/ML solutions for conduct risk and compliance use cases by translating typologies and red flags into measurable model outcomes. Drive research in supervised, unsupervised, and semi-supervised learning, graph analytics, and anomaly detection to improve true-positive rates and reduce false positives. Execute the full model lifecycle, including data sourcing, feature engineering, training, evaluation, calibration, and performance monitoring. Implement interpretable workflows with explainability tools and feedback loops with investigators. Partner with technology teams on MLOps, CI/CD, and automated monitoring for scalable deployment. Support model risk management deliverables by producing documentation for validation and addressing review feedback. Collaborate across stakeholders to align on requirements and target operating models. Apply GenAI pragmatically while prioritizing classical ML methods for defensible detection efficacy.
Required Qualifications
- Master's degree
- PhD
- 4 years of hands-on AI/ML experience
- exposure to financial crime compliance / conduct risk / AML / fraud / sanctions or similar control environments
- Demonstrated experience building and/or deploying ML solutions
- Strong Python skills
- experience with modern ML frameworks (e.g., PyTorch/TensorFlow)
- experience with common data/ML tooling
- Practical knowledge of: imbalanced learning, cost-sensitive evaluation, feature engineering, model calibration/threshold optimization, and performance measurement in detection settings
- Working knowledge of MRM expectations (documentation, validation support, explainability, monitoring) in regulated financial services environments
- Clear communication skills—able to explain model behavior, tradeoffs, and outputs (including reason codes) to technical and non-technical stakeholders
- Ability to mentor junior team members through code reviews, pairing, and technical guidance
Desired Qualifications
- Master's degree (or PhD preferred) in a quantitative discipline (Computer Science, Statistics, Mathematics, Economics, Operations Research, or related)
- experience with risk scoring, anomaly detection, triage/prioritization, NLP/LLM-enablement
- focus on measurable outcomes
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