Vice President - Data Science / Applied AI ML

Find Data Science Jobs; Hyderabad; On-site; INR 1,500,000 - 2,300,000; Full time

Job summary

Find Data Science Jobs seeks a senior data science leader to design, deploy, and operate production-grade GenAI/AI/ML solutions across risk and compliance use cases. You will drive research in advanced learning methods and build scalable MLOps pipelines to ensure regulatory-aligned, measurable control effectiveness.

You will own end-to-end model lifecycle, partner with Risk/Compliance, Investigations, Operations, and Technology, and lead the translation of typologies and red flags into

Qualifications

• Master’s or PhD in a quantitative discipline (CS, Stats, Math, Economics, OR).
• 7+ years hands-on GenAI/AI/ML in Financial Crime Compliance, AML, sanctions, fraud, or risk domains.

Responsibilities

• Lead GenAI/AI/ML initiatives to design, deploy, and operate production-grade solutions across risk and compliance.
• Drive research in supervised/unsupervised/semi-supervised learning, graph analytics, anomaly detection, and weak supervision.
• Own end-to-end model lifecycle: framing, data sourcing, feature engineering, model development, validation, monitoring, retraining.
• Maintain rigorous model risk management across the lifecycle with Risk and Internal Audit.
• Build robust MLOps pipelines, model registries, automated monitoring, and governance artifacts.

Skills

Leadership

Communication

Mentorship

Python

Education

Master’s or PhD in a quantitative discipline

Tools

Python

ML frameworks (TensorFlow, PyTorch)

Job description

• Lead the CCOR Conduct Data Science initiatives to design, deploy, and operate production-grade GenAI/AI/ML solutions across risk and compliance use cases, with a strong focus on measurable risk mitigation and regulatory alignment.
• Drive research and applied innovation in supervised/unsupervised/semi‑supervised learning, graph/network analytics, anomaly detection, and weak supervision to improve true‑positive rates, reduce false‑positives, and enhance investigator productivity.
• Own end‑to‑end model lifecycle: problem framing, data sourcing/controls, feature engineering (customer/behavioral/temporal/graph features), model development, validation, calibration/thresholding, bias/fairness checks, monitoring, and retraining.
• Maintain rigorous model risk management practices across Model lifecycle, partnering with Model Risk and Internal Audit.
• Build and maintain robust MLOps pipelines (CI/CD for ML), model registries, automated monitoring (data drift, concept drift, performance), and governance artifacts to ensure reliable, scalable production operations.
• Partner with Risk and Compliance (RCC), Investigations, Operations, and Technology to translate typologies, red flags, and regulatory expectations into defensible ML controls and measurable control effectiveness.
• Enhance decisioning through interpretable ML: deploy explainability techniques (e.g., SHAP, LIME, counterfactuals), stable reason codes, and human‑in‑the‑loop feedback loops to continuously improve model precision and usability.
• Maintain a pragmatic view of GenAI/LLMs as complementary tools (e.g., narrative generation for cases, unstructured doc parsing) while prioritizing classical/statistical/graph ML methods for core detection efficacy.

Job Responsibilities

• Lead the CCOR Conduct Data Science initiatives to design, deploy, and operate production-grade GenAI/AI/ML solutions across risk and compliance use cases, with a strong focus on measurable risk mitigation and regulatory alignment.
• Drive research and applied innovation in supervised/unsupervised/semi‑supervised learning, graph/network analytics, anomaly detection, and weak supervision to improve true‑positive rates, reduce false‑positives, and enhance investigator productivity.
• Own end‑to‑end model lifecycle: problem framing, data sourcing/controls, feature engineering (customer/behavioral/temporal/graph features), model development, validation, calibration/thresholding, bias/fairness checks, monitoring, and retraining.
• Maintain rigorous model risk management practices across Model lifecycle, partnering with Model Risk and Internal Audit.
• Build and maintain robust MLOps pipelines (CI/CD for ML), model registries, automated monitoring (data drift, concept drift, performance), and governance artifacts to ensure reliable, scalable production operations.
• Partner with Risk and Compliance (RCC), Investigations, Operations, and Technology to translate typologies, red flags, and regulatory expectations into defensible ML controls and measurable control effectiveness.
• Enhance decisioning through interpretable ML: deploy explainability techniques (e.g., SHAP, LIME, counterfactuals), stable reason codes, and human‑in‑the‑loop feedback loops to continuously improve model precision and usability.
• Maintain a pragmatic view of GenAI/LLMs as complementary tools (e.g., narrative generation for cases, unstructured doc parsing) while prioritizing classical/statistical/graph ML methods for core detection efficacy.

Required Qualifications and Skills

• 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