Location: Bengaluru, IndiaSummary: Own a multi-year, cross-functional AI transformation spanning control, finance, risk and related operations, and our global capability centres. You will build global connectivity, curate and prioritize an enterprise AI portfolio, evaluate synergies, and translate opportunities into scalable roadmaps and execution plans. Partner closely with product, engineering, technology, and operations to deliver measurable value while ensuring transparency, compliance, and responsible AI.Key ResponsibilitiesPortfolio leadership and opportunity sourcingProactively identify high-impact AI/ML opportunities across functions and regions; craft value theses, business cases, and clear success metrics.Assess feasibility, dependencies, and alignment with strategy; quantify benefits (cost, risk reduction, cycle-time, accuracy) and change impact.Map and exploit synergies across use cases (shared data, platforms, models, workflows) to maximize reuse and reduce duplication.Roadmaps, prioritization, and governanceBuild phased roadmaps with milestones, resource plans, and expected outcomes; apply transparent prioritization criteria: value, risk, readiness, feasibility.Establish program governance, OKRs, stage gates, and benefits realization; run quarterly portfolio reviews with senior leadership.Ensure adherence to model risk management, privacy, security, and responsible AI policies in partnership with Risk, Legal, and Compliance.Execution and change managementConvert opportunities into delivery plans; manage timelines, budgets, vendors, and inter-team dependencies.Lead pilots to scaled production with product managers and engineering; embed governance/SDLC, data quality controls, and integration into enterprise workflows.Drive change management, training, and operating-model updates to ensure adoption and sustained value realization.Page 2 of 3Determine functions that need to be in place to manage, govern and execute AI-native and AI-augmented solutions (e.g., monitoring and testing function)Execute creation of such functions with required budgeting discussions, mobility opportunities with upgraded skillsTransparency, communication, and stakeholder influenceCreate executive dashboards covering value capture, delivery health, risk posture, and adoption; communicate progress, challenges, and decisions crisply.Build a global community of practice across control, finance, risk, and GCCs to promote knowledge-sharing and asset reuse.Influence senior stakeholders and resolve cross-functional trade-offs quickly and constructively.Innovation and thought leadershipScout emerging AI capabilities and patterns relevant to financial services controls, risk, accounting and related operations; assess buy vs build and vendor ecosystems.Define reference architectures, data/product standards, and delivery playbooks to increase velocity and quality.Champion responsible AI, experimentation with guardrails, and continuous improvement.Risk and dependency managementAnticipate program risks (model, data, operational, third-party, change) and implement mitigation plansAlign with control partners early to streamline approvals and reduce rework.Page 3 of 3
Qualifications
15+ years in program management or product leadership at the intersection of technology and operations within financial services or other regulated industries.Proven track record leading large-scale AI/ML or analytics transformations from discovery to scaled adoption across global teams.Strong understanding of finance, operations, risk and control domains (e.g., reconciliations, financial close, FP&A, surveillance, KYC/AML, quality/control monitoring).Hands-on familiarity with data platforms, ML lifecycle, model governance, and enterprise workflow integration; effective partnership with product and engineering.Expertise in portfolio management, business casing, benefits tracking, and Agile-at-scale delivery.Exceptional communication and stakeholder management skills; able to simplify complex topics and drive consensus.Experience with change management and operating model design; commitment to responsible AI and compliance.Bachelor’s degree required; advanced degree in business, analytics, or engineering preferred.Success MetricsQuantified value realized (cost savings, risk reduction, revenue uplift, cycle-time and accuracy improvements).Conversion rate from pilots to scaled adoption; time-to-value.Reuse rate of shared capabilities; reduction in duplicative efforts.Compliance with AI risk, privacy, and security standards.Adoption and stakeholder satisfaction across regions and functions.Goldman Sachs is an Equal Opportunity Employer
