Make your mark modernizing business intelligence with artificial intelligence-driven insights, enterprise visibility, and strong career mobility.
As a Business Intelligence — AI & Advanced Analytics Vice President within Commercial Investment Banking, you will lead a high-velocity function that converts data into decisions, balancing approximately 60% hands-on delivery with 40% strategic leadership. You will partner closely with Data Engineering to build governed logical and semantic layers, elevate visualization in Sigma and Tableau, and operationalize large language model-powered natural language querying through tools such as Databricks Genie. You will measure success through decision velocity, adoption, and return on investment, anchored by an insight-to-action governance model that assigns ownership and tracks outcomes.
Job Responsibilities:
- Partner with senior and executive stakeholders to align analytics priorities to strategy, surface forward-looking insights, and influence outcomes through strong engagement.
- Drive end-to-end delivery across the analytics lifecycle, from problem framing and success criteria through user acceptance testing, deployment, adoption, and impact measurement with clear ownership and service-level agreements.
- Architect and govern the semantic layer by defining logical structures, business rules, and metric definitions for Engineering to implement, while coaching modeling trade-offs and performance optimization.
- Implement artificial intelligence-enabled business intelligence by enabling natural language querying (for example, Databricks Genie), designing domain-specific assistants, and embedding predictive analytics into decision flows as maturity grows.
- Establish visualization standards and personally build and review high-impact Sigma and Tableau assets that emphasize usability, performance, and guided analysis.
- Run an insight-to-action governance model that prioritizes findings, assigns accountable owners, tracks outcomes to closure, and communicates benefits, trade-offs, and risks transparently.
- Quantify and track portfolio impact metrics including adoption, decision velocity, decision quality, and return on investment, applying disciplined risk-adjusted prioritization.
- Orchestrate change management and enablement to drive adoption, including training, quick-reference content, and executive-ready briefings.
- Develop team capability through upskilling, code and modeling reviews, visualization critiques, and recruiting hybrid talent with domain and technical depth.
- Refine a continuous improvement backlog by iterating post go-live based on feedback and decommissioning low-value artifacts.
Required qualifications, skills, and capabilities:
- Demonstrate 10+ years of experience delivering business intelligence or analytics solutions.
- Show 3+ years of leadership delivering enterprise-scale business intelligence capabilities with measurable outcomes.
- Apply mastery of logical and semantic data modeling, semantic layer design, and metric stewardship.
- Build and optimize Sigma and Tableau assets, including performance tuning, governed self-service, and row-level security.
- Write advanced SQL (Structured Query Language) to analyze, validate, and troubleshoot complex datasets.
- Develop Python solutions to support analytics delivery, automation, or data quality use cases.
- Operationalize large language models and natural language processing within business intelligence workflows, including prompt engineering and Databricks Genie.
- Govern data definitions and metadata through disciplined documentation and stewardship practices.
- Use applied statistics and hypothesis testing to support sound measurement and decision-making.
- Translate ambiguous stakeholder asks into precise analytical requirements, success criteria, and testable outcomes.
- Communicate executive-ready narratives that translate complex analytics into actionable, well-controlled decisions while influencing cross-functional partners.
Preferred qualifications, skills, and capabilities:
- Deploy natural language querying over governed data in a way that supports scalable adoption and consistent metric interpretation.
- Build domain-specific artificial intelligence assistants aligned to business taxonomy and governed metric definitions.
- Drive adoption at scale with measurable return on investment and outcome tracking tied to decision-making.
Demonstrated ability to identify opportunities for AI and automation integration within operational workflows, including experience evaluating, implementing, or governing AI-driven solutions to achieve scalable process improvements and strategic objectives.