Job description

Job Purpose	We are looking for a leader who can build and run AI product Pods for Credit Risk, Fraud Risk Management, and collection & recovery. This role will power next-generation decisioning across lending lifecycle- understanding lifecycle- underwriting, portfolio monitoring, fraud controls, early warning, allocation strategy optimization, and recovery uplift – by delivering production-grade ML systems with measurable impact. This is highly cross-functional role requiring deep technical leadership, strong execution discipline, and hands-on experience operating large scale distributed machine learning frameworks. (Training + fine-tuning + serving) under BFSI governance, security, and model risk constraints.
Duties and Responsibilities	Own the AI Pod operating model across Credit Risk, Fraud/FRM, and collections/Recovery: outcomes, roadmap, delivery cadence, and cross-team dependencies. · Lead end-to-end model lifecycle: problem framing, feature strategy, training, evaluation, development, monitoring, and continuous improvement with clear scorecards per use-case. · Build large-scale ML systems: distributed training pipelines, feature stores, model registry, CI/CD for ML, and scalable batch + near-real-time scoring services. · Deliver Credit & Risk models: application/behavior risk models, limit assignment, early warning signals, portfolio monitoring, and policy optimization. · Deliver Frau & FRM systems: fraud propensity/risk scoring, anomaly detection, identity/device/channel signals using Graph Machine Learning. · Deliver collection & recovery optimization: roll-rate/cure/flow models, contactability, propensity-to-pay and recovery forecasting. · Define operating models: SLIs/SLOs, incident response, and stakeholder cadence. · Hire, develop, and scale the team: drive standards for quality, safety, and reliability.
Required Qualifications and Experience	Basic Qualifications: · Bachelor’s/Master’s in CS/Math/Engineering (PhD preferred in Large scale Machine learning systems) · 10+ years experience in Data Science /Applied ML/ ML Engineering with proven leadership delivering production – grade ML system at scale. · Demonstrated success shipping models with measurable business impact in credit risk, fraud/FRM, and /or collection & recovery. Required Skills & Competencies Core (must-have) · Large-scale model training & Fine-tuning: experience with distributed training, efficient fine -tuning patterns, model versioning, reproducibility, and cost/performance trade-offs. · ML evaluation rigor: calibration, stability/drift, bias/fairness checks, leakage prevention, robust back-testing, and champion-challenger frameworks. · Production mindset: ability to translate business objectives into ML systems with strong monitoring, alerting, and operational playbooks. Engineering & Tooling · Strong coding ability in Python (and Java/Scala as needed); ability to prototype rapidly and productize. · Distributed systems knowledge: scaling, caching, sharding, HA, performance tuning for both training and serving. · Experience with common stacks: feature stores, model registry, experiment tracking, vector/graph where relevant, stream/batch processing Kubernetes, CI/CD. · Observability: logging/metrics/tracing, incident management, SLO-driven operations. · Experience with data and compute stacks: Spark, Kafka/streaming, Lakehouse/warehouse, APIs/microservices. Governance, Security, and Compliance · Designing for BFSI constraints: PII handling, policy enforcement, auditability, access controls. · Risk-aware engineering mindset: safe tool execution, approval workflows, and secure-by-design, approval workflows, and secure by design patterns. Leadership Behaviors · High ownership, structured thinking, and ability to drive clarity in ambiguous environments. · Strong program management

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Someone shared one job with you. ASAI keeps finding the rest.

Scored, not searched. Every role ranked against your actual profile.

Alerts as often as hourly. Reach new roles while the pile is still small.

Skill gaps, spelled out. See exactly which requirements you don't meet yet.

Verified jobs, only. Say no to ghost jobs. Your time deserves respect.

Job description

Job Purpose	We are looking for a leader who can build and run AI product Pods for Credit Risk, Fraud Risk Management, and collection & recovery. This role will power next-generation decisioning across lending lifecycle- understanding lifecycle- underwriting, portfolio monitoring, fraud controls, early warning, allocation strategy optimization, and recovery uplift – by delivering production-grade ML systems with measurable impact. This is highly cross-functional role requiring deep technical leadership, strong execution discipline, and hands-on experience operating large scale distributed machine learning frameworks. (Training + fine-tuning + serving) under BFSI governance, security, and model risk constraints.
Duties and Responsibilities	Own the AI Pod operating model across Credit Risk, Fraud/FRM, and collections/Recovery: outcomes, roadmap, delivery cadence, and cross-team dependencies. · Lead end-to-end model lifecycle: problem framing, feature strategy, training, evaluation, development, monitoring, and continuous improvement with clear scorecards per use-case. · Build large-scale ML systems: distributed training pipelines, feature stores, model registry, CI/CD for ML, and scalable batch + near-real-time scoring services. · Deliver Credit & Risk models: application/behavior risk models, limit assignment, early warning signals, portfolio monitoring, and policy optimization. · Deliver Frau & FRM systems: fraud propensity/risk scoring, anomaly detection, identity/device/channel signals using Graph Machine Learning. · Deliver collection & recovery optimization: roll-rate/cure/flow models, contactability, propensity-to-pay and recovery forecasting. · Define operating models: SLIs/SLOs, incident response, and stakeholder cadence. · Hire, develop, and scale the team: drive standards for quality, safety, and reliability.
Required Qualifications and Experience	Basic Qualifications: · Bachelor’s/Master’s in CS/Math/Engineering (PhD preferred in Large scale Machine learning systems) · 10+ years experience in Data Science /Applied ML/ ML Engineering with proven leadership delivering production – grade ML system at scale. · Demonstrated success shipping models with measurable business impact in credit risk, fraud/FRM, and /or collection & recovery. Required Skills & Competencies Core (must-have) · Large-scale model training & Fine-tuning: experience with distributed training, efficient fine -tuning patterns, model versioning, reproducibility, and cost/performance trade-offs. · ML evaluation rigor: calibration, stability/drift, bias/fairness checks, leakage prevention, robust back-testing, and champion-challenger frameworks. · Production mindset: ability to translate business objectives into ML systems with strong monitoring, alerting, and operational playbooks. Engineering & Tooling · Strong coding ability in Python (and Java/Scala as needed); ability to prototype rapidly and productize. · Distributed systems knowledge: scaling, caching, sharding, HA, performance tuning for both training and serving. · Experience with common stacks: feature stores, model registry, experiment tracking, vector/graph where relevant, stream/batch processing Kubernetes, CI/CD. · Observability: logging/metrics/tracing, incident management, SLO-driven operations. · Experience with data and compute stacks: Spark, Kafka/streaming, Lakehouse/warehouse, APIs/microservices. Governance, Security, and Compliance · Designing for BFSI constraints: PII handling, policy enforcement, auditability, access controls. · Risk-aware engineering mindset: safe tool execution, approval workflows, and secure-by-design, approval workflows, and secure by design patterns. Leadership Behaviors · High ownership, structured thinking, and ability to drive clarity in ambiguous environments. · Strong program management