About the role
structured by ORILead AI-enabled analytics that turns governed data into faster decisions and measurable value. Make your mark modernizing business intelligence with artificial intelligence-driven insights, enterprise visibility, and strong career mobility.
What you will do
- 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.
What they are looking for
- 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.
Nice to have
- 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.
Full posting text
Lead AI-enabled analytics that turns governed data into faster decisions and measurable value.
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.