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Open nowPosted today

Head of Data AI & Analytics

U.S. Bank1,247 open roles

Where
Chennai, India
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Your applicationOpen nowHead of Data AI & AnalyticsU.S. Bank · Chennai, India

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The clock on this job

Early applications get read.

13.8% of postings close within 7 days. Measured by our own scanner across the market. U.S. Bank postings stay open a median of 23 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.7%3 days
  3. 13.8%7 days
  4. 22.3%14 days
  5. 37.8%30 days
  6. 59.0%60 days
This job: posted today

U.S. Bank median: 23 days open

The posting

At U.S. Bancorp India, we’re on a journey to do our best. We believe it takes all of us to bring our shared ambition to life, and each person is unique in their potential. A career with U.S. Bancorp India gives you a wide, ever-growing range of opportunities to discover what makes you thrive at every stage of your career. Try new things, learn new skills and discover what you excel at—all from Day One.

Job Description

Role Overview

The GCC Head of Data, AI & Analytics is accountable for building and leading the enterprise Data, AI and Analytics capability in India. The role brings together the GCC responsibilities of the Enterprise Data Office (EDO) with business-facing AI and analytics work transitioning from across the enterprise, creating a single point of leadership and accountability in the GCC.

This is a build and scale role. The leader will establish an integrated organization spanning data engineering, data products, governance, quality, master data management, data operations, business analysis, data science, algorithms, advanced analytics, AI use-case development and program management. The mandate includes consolidating analytics work that is currently distributed across the enterprise as it moves to the GCC, while preserving strong alignment to the businesses and functions the teams support.

The role is distinct from the GCC AI Technology organization. This leader owns the business-facing AI lifecycle, including problem definition, data readiness, data models, analytical methods, algorithms, data science and business adoption. The AI Technology team owns the technology engineering, platforms and technical enablement required to deploy and operate AI solutions. The two leaders will work as peers to deliver integrated outcomes.

Key Responsibilities

Enterprise Capability and Delivery Leadership

· Establish and lead the integrated Data, AI & Analytics operating model for the GCC, supporting all EDO pillars and enterprise business lines.

· Build scalable delivery teams and common practices that improve execution consistency, quality and speed across the portfolio.

· Align GCC capacity and capabilities to enterprise data, AI and analytics strategies, roadmaps and business priorities.

· Lead enterprise-scale planning across multiple portfolios and maintain a consolidated view of commitments, dependencies, risks, capacity and outcomes.

· Balance business-aligned delivery with enterprise standards, ensuring teams remain close to stakeholders while operating as one coherent GCC capability.

Data, AI and Analytics Leadership

· Create a single GCC home for analytics work transitioning from business lines and enterprise functions, reducing fragmentation while maintaining clear business ownership of priorities and outcomes.

· Lead the business-facing AI lifecycle: opportunity identification, use-case framing, data discovery, data preparation, feature and data model development, algorithm design, experimentation, model evaluation, insight generation and adoption.

· Build and scale data science, decision science, advanced analytics and business analytics capabilities that address enterprise and line-of-business priorities.

· Establish standards for analytical methods, development practices, documentation, validation, reproducibility, responsible AI and outcome measurement in partnership with enterprise Risk, Model Risk, Legal and Compliance stakeholders.

· Partner with the GCC Head of Technology and the AI Technology leader on platforms, architecture, engineering, deployment, production support and lifecycle management, with clear handoffs and shared accountability.

· Drive reuse of data assets, features, algorithms, analytical products, frameworks and accelerators across use cases and business lines.

EDO Pillar Enablement

· Partner with Data Strategy & Administration leadership to execute the enterprise data strategy and operating model, supported by strong program management and business operations.

· Provide product managers, delivery leaders, business analysts, scrum leadership and data engineering capacity to accelerate Data Product Enablement & Execution and onboarding of new data products.

· Scale Master Data Management through MDM analysts, data stewards, implementation specialists and support teams across priority domains.

· Build innovation and proof-of-concept teams for AI, GenAI and advanced analytics, and establish reusable capability centers where enterprise demand supports them.

· Supply data operations engineers and platform specialists, standardize operational processes and strengthen service management.

· Provide governance analysts, metadata specialists and data-quality resources to support Data Risk & Governance, enterprise remediation and compliance priorities.

Workforce and Capability Development

· Own the GCC workforce strategy across data engineering, data product management, governance, quality, MDM, data operations, analytics, data science, AI, business analysis and program management.

· Lead hiring, onboarding, deployment, performance enablement, talent development and succession planning for the organization.

· Define role standards, competency frameworks, career paths and learning programs that support consistent hiring and progression across disciplines.

· Establish flexible staffing models and optimize capacity across EDO pillars, enterprise functions and business priorities.

· Partner with Talent Acquisition, HR and Compensation to build a differentiated talent proposition for scarce data, AI and analytics skills in India.

Program Execution, Governance and Risk

· Establish a centralized PMO and portfolio governance model for the GCC Data, AI & Analytics organization.

· Define common standards for project and product delivery, agile execution, financial reporting, KPI measurement, risk management and executive reporting.

· Lead monthly and quarterly planning and execution reviews, including portfolio-level issue resolution and escalation management.

· Ensure adherence to applicable regulatory requirements and the bank's data, model, AI, privacy, risk, compliance and control expectations.

· Partner with Risk, Audit, Legal, Compliance and Model Risk Management as capabilities scale and the GCC control environment matures.

GCC Operating Model and Transformation

· Define delivery location, sourcing and transition strategies for data, AI and analytics work moving to the GCC.

· Maintain continuity and control as work transitions from distributed enterprise teams and vendor-based models into the GCC.

· Establish vendor and managed-service governance, with clear accountability for outcomes, quality, knowledge transfer and cost.

· Create reusable accelerators, templates, frameworks and best practices; drive automation and continuous improvement across the organization.

· Measure GCC effectiveness through productivity, quality, speed to delivery, adoption, business outcomes, control performance, and stakeholder satisfaction.

Executive Stakeholder Management

· Serve as the senior GCC execution partner to EDO pillar heads and enterprise business and functional leaders for data, AI and analytics.

· Build trusted relationships with U.S.-based Data, AI, Analytics, Product, Technology, Risk and business leadership.

· Chair cross-pillar execution reviews and facilitate dependency management across portfolios.

· Provide clear executive reporting on delivery health, capacity, financials, risks, adoption, and outcomes.

· Promote a culture of collaboration, accountability, responsible innovation, and continuous improvement.

Basic Qualifications

· Significant experience leading enterprise data, AI, data science, advanced analytics or related functions at scale.

· Demonstrated experience leading leaders and building multi-disciplinary teams across data, analytics and business-facing AI.

· Experience operating in a global delivery, GCC or captive center environment, including transition and scale-up of work.

· Deep working knowledge of modern data management, analytics and AI lifecycles, including data products, governance, quality, modeling, algorithms and adoption.

· Experience working in a regulated environment with strong risk, control, privacy and governance expectations.

Preferred Skills and Experience

· Experience establishing or materially scaling a data, AI or analytics capability in India, including hiring scarce skills at pace.

· Strong understanding of the India talent market for data engineering, data science, AI, analytics, data product and governance roles.

· Ability to translate business priorities into a portfolio of data, analytical and AI outcomes and to influence senior stakeholders across geographies without direct authority.

· Experience consolidating fragmented analytics teams or work into a coherent operating model while preserving strong business partnership.

· Working knowledge of responsible AI, model governance, data privacy, metadata, lineage, data quality and master data management.

· Financial services experience and familiarity with enterprise risk, compliance, audit and model risk expectations.

· Experience managing organizational change, vendor transitions, and movement of work between delivery models.

If there’s anything we can do to accommodate a disability during any portion of the application or hiring process, please refer to our disability accommodations for applicants.

Posting may be closed earlier due to high volume of applicants.

This is an U.S. Bancorp India posting. U.S. Bancorp India is a part of the U.S. Bank family.

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