We have an exciting opportunity for you to enhance your career in Data & Analytics Product Strategy & Execution teams, contributing to innovative banking solutions.
As a Analytics Solutions Manager within Data & Analytics Product Strategy & Execution team, you will serve as a strategic advisor and execution orchestrator, translating enterprise objectives into a cohesive portfolio strategy, shaping cross-product initiative charters, and driving disciplined delivery and value realization. You will operate at the intersection of product, data/AI, technology, and business leadership to ensure alignment, prioritization, and adoption of AI capabilities at scale.
The Data & Analytics (D&A) Product Strategy & Execution team drives the strategy, governance, and delivery of high-priority AI initiatives across products and lines of business—ensuring measurable outcomes at enterprise scale.
Job responsibilities
Portfolio Strategy & Value Realization: Translate enterprise priorities into a portfolio strategy and operating cadence for AI initiatives, with clear outcomes, KPIs/OKRs, and value tracking.
Strategic Initiative Shaping (Discovery → Delivery): Partner with Product and Business leaders to define initiative charters (problem statement, scope, success metrics, “definition of done,” dependencies, and delivery approach).
Executive Advisory & Consultative Leadership: Frame ambiguous, complex problems; surface tradeoffs and constraints; guide leaders toward decisions that maximize impact, speed-to-value, and risk-aware execution.
Cross-Product Governance & Decision Forums: Establish and moderate governance (steerco/forums) to drive timely, evidence-based decisions and resolve risks, issues, and interlocks.
Roadmap & Dependency Orchestration: Co-develop integrated roadmaps across products, sequencing milestones, releases, and dependencies to optimize delivery and unlock incremental value.
Delivery Transparency & Insight-Driven Reporting: Provide regular leadership updates with narrative and metrics—progress, outcomes, risks, value indicators, and recommended course corrections.
Risk, Controls, and Compliance Partnership: Proactively identify risks (delivery, data, model, operational, regulatory) and coordinate mitigation plans with appropriate risk/control partners.
Capacity & Prioritization Discipline: Drive intake, prioritization, and resource alignment for cross-product initiatives, ensuring investment is directed to the highest strategic value opportunities.
Adoption & Change Enablement: Ensure initiatives translate into business adoption (enablement, stakeholder readiness, rollout planning, measurement of usage and impact).
Culture of Strategic Collaboration: Foster a high-performing, continuous-improvement culture that challenges assumptions and promotes clarity, accountability, and outcomes.
Required qualifications, capabilities, and skills
Bachelor’s degree required (or equivalent experience) 10+ years in portfolio/program management, product execution, or strategic initiative leadership—ideally within data, analytics, and/or AI environments.
Demonstrated success leading cross-functional, cross-product initiatives in a matrixed organization, with strong execution rigor and strategic orientation.
Strong stakeholder management and influence skills, including experience supporting senior/executive decision-making and facilitating governance forums.
Proven ability to define charters, build integrated roadmaps, manage dependencies, and deliver outcome-based reporting (KPIs/OKRs and value realization).
Experience driving enterprise platform/capability adoption (enablement, operating model design, measurement of adoption and impact).
Knowledge of AI/ML concepts and scaling considerations (data readiness, MLOps/ModelOps, responsible AI, monitoring).
Excellent written and verbal communication; able to synthesize complex topics into crisp executive narratives and actions.
Preferred qualifications, capabilities, and skills
- Experience in a large, matrixed financial services or technology organization.
- Familiarity with portfolio management and product execution frameworks/tools (e.g., agile/lean delivery, OKR-based planning, dependency management).
- Exposure to data governance, regulatory expectations, and risk management in AI/ML contexts.