Osaic Careers
Data Governance & AI Enablement Opportunity in Financial Services
Sr. Director, Data Governance & AI Enablement
Location(s):
Birmingham: 3535 Grandview Pkwy #500 Birmingham, AL 35243
Osaic has returned to the office on a hybrid schedule requiring a minimum of 4 days weekly in the office. Non-remote employees must be willing to work this schedule. Given the regional and travel requirements for this position, Osaic is open to remote applicants for this position.
Role Type: Full-time, Exempt.
Salary: Compensation is comprised of a base salary and annual performance. Actual compensation offered will be determined individually, based on several job-related factors, including location, skills, licensure, experience, and education.
Our competitive compensation is just one component of Osaic’s total compensation package. Additional benefits include health, vision, dental insurance, 401k, paid time away, volunteer days and much more. To view more details of what you can look forward to, visit our careers page: Osaic Benefits.
Summary:
The Sr. Director, Data Governance & AI Enablement advances enterprise data governance maturity and ensures the organization’s data is trusted, controlled, and AI-ready. This role owns the enterprise data governance framework and bridges data governance, AI strategy, and business execution to enable scalable, responsible AI adoption through strong data foundations, clear policies, and embedded operating discipline. The role also helps modernize analytics for an AI-native future and supports enterprise AI execution through cross-functional coordination, vendor and RFP support, build-versus-buy assessment, and an enterprise view of business and financial value. This leader partners closely with AI Product, AI Experience, Technology, Risk, and business stakeholders. The role owns data governance and AI-readiness foundations, supports broader enterprise AI execution, and complements—but does not replace—AI Product ownership of roadmap and platform lifecycle or AI Experience ownership of adoption and experience quality.
Education Requirements:
Bachelor's Degree Preferred, H.S. Diploma or GED certificate+ Significant Practical Experience will be considered.
Responsibilities:
Enterprise Data Governance Leadership
Own and evolve the enterprise data governance framework, including policies, standards, stewardship, the domain operating model, and supporting platform capabilities. Drive identification and operationalization of Critical Data Elements, establish scalable processes for business glossary, data ownership, and decision rights, and lead evaluation and implementation of a data and analytics governance platform aligned to enterprise needs.
Data Quality & Issue Management
Define enterprise data quality standards, thresholds, and scorecards tied to business impact. Establish consistent issue intake, triage, and remediation process, and partner with Technology and business teams to drive root-cause resolution and sustained improvement.
AI Data Readiness & Governance
Define and enforce data governance standards for AI use cases, including training data, semantic layers, and knowledge sources. Ensure traceability from data to model to output through lineage, controls, and auditability, and partner with AI Product and Technology to confirm data readiness before deployment. Establish consistent business definitions so AI-enabled analytics and decision support are grounded in trusted, explainable data.
AI Governance (Business & Data Layer)
Establish business-side governance controls for AI data usage, access, and quality. Embed responsible AI principles into governance processes with Risk, Legal, and Compliance, and define human-in-the-loop accountability and stewardship expectations for AI-enabled decisions.
Operating Model Integration
Embed governance into delivery processes, including ARTs, programs, and the product lifecycle, so it is operational rather than theoretical. Drive alignment across federated data domains and partner with Technology to integrate governance into platforms, workflows, and tooling.
Business Value & AI Enablement
Align governance priorities to enterprise AI strategy and measurable business outcomes such as advisor productivity, cost-to-serve, and client experience. Enable trusted AI adoption through consistent definitions, high-quality data, and fit-for-purpose controls. Support analytics modernization and work alongside Technology and technology custodians on implementation of the enterprise data warehouse and semantic layer in Snowflake. Provide an enterprise view of AI opportunities across front-office and back-office use cases, and help frame build-versus-buy decisions, vendor assessments, and financial value in partnership with AI Product, AI Experience, Technology, and business leaders.
Enterprise AI Coordination & Execution Support
Support enterprise AI initiatives beyond the data layer by helping translate strategy into coordinated execution across business, product, technology, and control stakeholders. Facilitate AI-related RFP and vendor evaluation efforts, help close cross-functional ownership or capacity gaps, and bring governance discipline, execution support, and business case thinking to enterprise AI priorities.
Scope & Team
Lead the Data Governance function, including the Data Governance Manager and supporting analysts. Provide matrix leadership to domain data stewards and influence stakeholders across Technology, AI Product, AI Experience, Risk, Legal, and Operations.
Requirements:
10+ years of experience in data governance, data management, or data strategy in financial services or another regulated industry.
Proven success scaling data governance in a federated organization.
Strong understanding of data quality, lineage, stewardship, governance operating models, and AI data-readiness requirements.
Working knowledge of AI and machine learning governance considerations; hands-on modeling experience is not required.
Demonstrated ability to lead through influence across business, technology, and control functions.
Strong executive communication skills focused on business value, outcomes, and pragmatic decision-making.
Experience modernizing analytics to support AI-enabled decisioning, natural language insight experiences, or semantic-layer-driven reporting.
Experience leading or supporting selection and implementation of data and analytics governance platforms, including requirements, capability assessment, and stakeholder alignment.
Experience with enterprise AI operating models, AI-related vendor or RFP processes, build-versus-buy assessments, and business case development.
Positions governance as an enabler of AI and business value, not only a control function.
Drives measurable improvements in data quality, AI readiness, and execution effectiveness.
Embeds governance into day-to-day delivery and decision-making.
Builds trust by balancing innovation, control, and practicality.
Provides effective enterprise coordination where cross-functional ownership, capacity, or alignment gaps could slow AI progress.