Director, Data, Analytics & AI - FR/ERM (Bengaluru)

Standard Chartered; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Standard Chartered in Bangalore, IND seeks a Director, Advanced Analytics & AI to lead design, build and industrialise analytics and ML solutions that enhance risk, regulatory compliance, and decision-making capabilities. The role spans the lifecycle from use case discovery to production deployment, aligning to governance expectations.

The position sits at the intersection of business, data products, and technology, with responsibility for end-to-end AI delivery and value-led outcomes.

Qualifications

• Strong hands-on experience in machine learning and analytics.
• Experience in risk and regulatory domains is a plus.
• Ability to lead cross-functional, cross-domain teams.

Responsibilities

• Lead the design, build, and industrialisation of advanced analytics and ML solutions for risk, compliance, and decision-making.
• Define end-to-end analytics architecture and governance across data, models, and deployment.
• Drive the transition from POC to enterprise-grade AI products with scalable pipelines and model management.

Job description

Requisition Number: 54409

Job Location: Bangalore, IND

Global Grade: Band 5

Work Type: Office Working

Employment Type: Permanent

Posting Start Date: 01/07/2026

Posting End Date: 21/08/2026

Job Summary

The Director, Advanced Analytics & AI is a techno-functional leader responsible for designing, building, and industrialising advanced analytics and machine learning solutions that enhance the banks financial risk management, regulatory compliance, and decision-making capabilities.

The role sits at the intersection of:

• Business (Risk / Compliance)
• CDO (data products)
• Technology (engineering & product ionisation)

and ensures an end-to-end lifecycle from use case discovery to production-grade deployment, aligned to regulatory and model governance expectations

Key Responsibilities

Strategy

• Lead identification, prioritisation, and shaping of high-impact analytics & ML use cases across Financial Risk and Compliance domains
• Translate regulatory and business requirements into analytical problem statements and solution blueprints
• Own business value realisation (efficiency, risk reduction, control effectiveness, insights)
• Aligns with CoE mandate to drive value-led, outcome-focused AI delivery.

Business

• Define end-to-end solution architecture for analytics and ML use cases:
• Feature engineering, model selection, evaluation strategy
• Data sourcing and transformation requirements
• Establish and enforce design patterns, reusable components, and modelling standards
• Reflects role of lead architect + capability owner in CoE model

Processes

• Personally lead or closely supervise the development of POCs and prototypes for:
• New analytical patterns
• Complex or regulatory-sensitive use cases
• Validate:
• feasibility
• performance
• explainability
• Core expectation: prototype validate scale recommendation
• Establish reusable:
• feature engineering pipelines
• model templates
• evaluation frameworks
• Drive scaling from POCs to enterprise-grade solutions
• Critical to avoid one-off analytics and move to repeatable AI products
• Define requirements for AI-ready data products with CDO teams:
• curated datasets
• feature stores
• data quality & lineage
• Ensure alignment between:
• data supply (CDO)
• analytics consumption (AI CoE)
• Aligns with CoE positioning as bridge between data and intelligence

People & Talent

• Lead through example and demonstrate the banks culture and values

Key Responsibilities

Risk Management

• Work with Technology and Data Engineering teams to industrialise solutions into production
• Provide oversight for:
• model integration
• pipelines, APIs, and deployment frameworks
• Ensure:
• functional correctness
• alignment to business intent
• Consistent with model:
• Risk/AI CoE owns logic, validation
• Technology owns runtime & engineering
• Embed analytics into:
• credit risk models
• stress testing & forecasting
• financial crime detection
• regulatory reporting analytics
• Ensure outputs are:
• explainable
• auditable
• regulator-ready

Governance

• Define and enforce end-to-end model lifecycle controls:
• model documentation and explainability
• validation frameworks
• monitoring (drift, bias, performance)
• Ensure compliance with:
• Model Risk Management
• AI governance, fairness, explainability
• Regulatory expectations on AI usage
• Strong emphasis on governed lifecycle and audit-readiness

Reporting & Stakeholder Communication

• Act as primary interface between business stakeholders, CDO, and Technology
• Engage senior stakeholders to:
• align priorities
• drive adoption
• manage regulatory expectations
• Role explicitly requires strong business-tech bridging capability

Team Leadership & Capability Building

• Lead multidisciplinary teams of:
• data scientists
• ML engineers
• analytics specialists
• Coach teams on:
• model development best practices
• regulatory constraints
• production readiness
• Build reusable:
• frameworks
• accelerators
• experimentation standards

Key Stakeholders

• Data & Analytics
• GenAI Specialists
• AI Operations
• Business Units
• Technology (AI Engineering Lead; Data Engineering Lead; platform owners)
• Functions CDO stakeholders (standards, platform, data foundations)
• AI Services
• Legal, Privacy, Cyber Security, Model Risk, Operational Risk
• Internal Audit / Assurance partners
• COO / Finance partners (capacity and investment planning)
• AI Solutions Team
• Compliance & Governance

Skills and Experience

Technical and Operational Skills

• Strong Hands-On Experience In:
• Machine Learning (Classification, Regression .