Wholesale Client Profitability (WCP), Transformation, Controllership & P&A - Vice President

Join a high-impact team modernizing client profitability through process transformation and stronger controllership. Lead the adoption of AI and next-generation automation to deliver faster, more trusted profitability insights. Partner globally across Finance, Technology, Data, and Risk to drive measurable outcomes and durable change.

As a/an VP within WCP Client Profitability Planning & Analysis, you will advance client profitability by improving the completeness, accuracy, and timeliness of profitability attribution and reporting. You will translate business needs into structured, controlled initiatives that standardize and automate profitability processes across inputs, allocations, and outputs. You will serve as the Bengaluru-based point of contact coordinating with global stakeholders to ensure high-quality delivery, adoption, and audit-ready execution.

Job Responsibilities 

• Drive operating model and process redesign for WCP with a focus on standardization, automation, and controls-by-design.
• Translate finance and business requirements into scoped roadmaps with milestones, dependencies, and measurable benefits.
• Partner with Technology and Data to rationalize tools, reduce manual touchpoints, and shorten cycle times while improving lineage and documentation.
• Strengthen controllership across profitability inputs, allocation methodologies, and outputs to improve integrity of results.
• Establish and monitor key controls and KPIs for completeness, accuracy, and timeliness (e.g., reconciliations, reasonableness checks, change control, issue management).
• Partner with Product Control and Financial Control teams to ensure alignment with accounting policies, allocation standards, and control expectations.
• Integrate AI-enabled solutions where they materially improve throughput, stability, and decision-usefulness without compromising governance.
• Lead identification, piloting, and deployment of AI/ML, GenAI, and intelligent automation use cases within period-end/close workflows.
• Define guardrails for AI-enabled processes, including explainability, data privacy, segregation of duties, access controls, fallback procedures, and post-release monitoring.
• Quantify and track benefits (e.g., cycle time reduction, manual control elimination, data quality improvement, stability) and drive adoption plans for end users.
• Coordinate cross-functional governance cadence and deliver executive-ready updates on progress, risks, decisions, and outcomes.

Required Qualifications, Capabilities, and Skills 

• Bring 12 years of experience in finance transformation, controllership, product control, and/or P&A within a complex institution.
• Demonstrate strong knowledge of profitability/performance measurement (revenues, costs, allocations, FTP/transfer pricing, balance sheet usage, returns).
• Deliver operating model redesign and process transformation with measurable improvements in automation, control strength, and cycle time.
• Apply hands-on AI/ML and modern automation within financial close/period-end processes, spanning requirements, design, UAT, and controlled deployment.
• Utilize advanced analytics to work with large datasets and connect financial outcomes to operational and data drivers, including advanced Excel and BI tooling.
• Lead matrixed workstreams across Finance, Technology, and Data with disciplined execution and ownership.
• Communicate crisply with executives and stakeholders, translating complex topics into clear decisions and actions.

Preferred Qualifications, Capabilities, and Skills

• Leverage experience supporting Wholesale or Corporate & Investment Bank businesses, including client/product profitability and pricing.
• Apply exposure to finance data products and lineage practices, including reconciliations, documentation, and post-release monitoring.
• Utilize AI/automation toolchains relevant to Finance (e.g., outlier detection, intake automation, workflow orchestration, governed GenAI assistants).
• Demonstrate familiarity with control frameworks for AI-enabled processes, including model risk management, explainability standards, and change management.
• Use close management platforms or AI-assisted period-end workflows that automate reconciliations, variance narratives, and task orchestration.