Head of AI Enablement

Qualifications

• Proven technical leadership:
• 10+ years of experience building and owning modern data platforms and AI-driven analytics solutions, with progressively increasing leadership scope
• Data integration & pipeline expertise:
• Proven experience integrating diverse systems and data sources via APIs (including ERP/CRM/HRIS systems and third-party data providers) and building data pipelines with enterprise-scale reliability
• Hands-on expertise in data ingestion and ETL/ELT processes, ensuring data quality and consistency across complex, large-scale datasets
• Analytics tools & technology skills:
• Deep familiarity with modern data technologies and tools
• Hands-on experience with cloud-based data architectures (Microsoft Azure/Microsoft Fabric or similar), data lakehouse/warehouse design, and data engineering frameworks
• Proficiency with business intelligence and visualization tools (e.g. Power BI or similar) for developing dashboards and enabling self-service analytics
• Working knowledge of machine learning and AI frameworks relevant to data analytics
• Proven ability to partner with senior executives and cross-functional teams, acting as the “translator” between business needs and technical execution
• Comfortable explaining complex data or AI concepts in business terms and aligning diverse stakeholders around a common vision
• Data governance & security:
• Strong knowledge of data governance, privacy, and security best practices
• Experience implementing role-based access controls, data classification, and audit logging to ensure compliance and trust in analytics outputs
• A track record of building systems that are secure and auditable by design (e.g. handling sensitive financial data in regulated environments) is highly valued
• Business acumen:
• Solid understanding of finance and accounting principles, and how operational data and AI-derived insights map to financial performance metrics (“score-keeping”)
• Ability to quickly grasp the client's investment strategies and translate business questions into data/AI solutions, ensuring technology efforts directly support the creation of alpha and efficiency in investment operations
• For example, familiarity with tools like Microsoft Copilot or other generative AI assistants, and a mindset of continuously evaluating new technologies (machine learning algorithms, AI services, etc.)
• Prior experience in investment management, private equity, or financial services is advantageous
• Understanding the workflows, regulatory considerations, and typical data challenges in the investment domain can help in quickly aligning the platform’s capabilities with the firm’s needs
• The ideal candidate will thrive in a fast-moving, results-driven environment and demonstrate a strong sense of ownership
• Entrepreneurial mindset: A proactive, hands-on style and “own it” mentality – willing to roll up your sleeves, collaborate across teams, and adapt to the dynamic, startup-like setting of a newly formed organization
• Commitment and resilience: Comfortable with a “whatever-it-takes” approach, including being available on occasional weekends or outside standard hours for urgent projects or critical deadlines
• You demonstrate resilience under pressure and keep the mission in focus

Benefits

• Competitive compensation with performance-based incentives
• Comprehensive health and retirement programs
• Generous paid time off (PTO)
• Support for professional development and continuous learning
• Significant influence in shaping the team and technology choices as the function grows (high degree of autonomy and impact)

Responsibilities

• You will own the enterprise data and AI strategy and build a modern analytics platform from the ground up
• In this role, you will define the roadmap to embed advanced analytics and AI capabilities into all aspects of the client's investment workflows, standardize data across diverse business entities, integrate critical systems via APIs, and ensure that leadership and operators have access to timely, trusted insights
• You’ll start as a hands-on builder developing key platform components and proofs of concept, and then evolve into the executive owner of our AI and data capabilities—ultimately leading a team and setting the strategic direction as the function grows
• Set the vision & roadmap:
• Translate business goals into a pragmatic, phased data and AI strategy and operating model covering data ingestion, modeling, governance, and advanced analytics
• Outline a multi-year roadmap that aligns technology initiatives with investment objectives and value creation opportunities
• Build & evolve the platform:
• Lead the architecture and development of the client's internal AI platform , encompassing the Portal, the Vault, and the Engine
• Ensure this platform is scalable and modular, with strong engineering standards and documentation, enabling new capabilities to be added without re-architecting
• Integrate critical systems:
• Design and manage integrations/APIs for core business systems (ERP, CRM, HRIS) and external data feeds (e.g. market data providers, SEC/EDGAR filings, economic APIs)
• Build scalable, reliable pipelines to ingest and normalize data (including unstructured documents via document AI techniques) into the Vault, providing a single source of truth ready for analysis and modeling
• Embed AI into workflows:
• Partner with investment, research, and operations teams to embed AI-driven insights into their daily processes
• Identify high-impact use cases (e.g. AI-assisted due diligence research such as automated document analysis, portfolio performance analytics, risk scenario simulations) and deliver user-friendly tools – from interactive dashboards to self-service portals – that allow non-technical stakeholders to leverage data and AI insights directly
• Continuously champion data/AI literacy, training team members and fostering adoption of new analytics capabilities across the firm
• Stabilize, standardize & secure:
• Establish robust data governance and engineering best practices to bring order to a rapidly evolving analytics environment
• Implement processes for data quality control, master data management, and consistent semantic definitions so that insights are fast, accurate, and trustworthy
• Architect security and compliance into every layer of the platform from day one – enforce strict role-based access controls (RBAC) and permissions, maintain comprehensive data lineage and audit logging for all data and AI outputs, and ensure every critical data point is traceable back to its source
• Orchestrate resources & partnerships:
• Coordinate with trusted consulting partners and technology vendors to accelerate development of the platform where appropriate
• Plan the evolution of internal resources by hiring and mentoring a high-caliber team (e.g. data engineers, ML engineers, and platform developers) as the capabilities mature
• Foster a culture of excellence within the team, and effectively allocate internal vs. external resources to meet ambitious deliverables and timelines
• Measure impact & iterate:
• Define key performance indicators to track platform success – for example, system reliability/uptime, pipeline processing times, user adoption rates, and tangible business outcomes such as faster deal evaluations or improved portfolio insights
• Rigorously measure and report these results to stakeholders
• Use data and feedback to iteratively refine the roadmap, ensuring that data and AI initiatives continue to align with business priorities and deliver meaningful value
• Exposure to Python and its data/ML ecosystem, NLP or document AI techniques, and emerging generative and agentic AI tools (LLMs)
• While this is a leadership role, you should be able to understand and guide the application of these technologies in a business setting, identifying opportunities to leverage AI for automation and insight generation
• Excellent communication and stakeholder management skills
• In-office collaboration: Willingness to be on-site in our Houston office Monday–Thursday (approximately 8:30am – 5:30pm) to engage deeply with the team and stakeholders

Full Description

Job Title: Head of AI Enablement

Location: Houston, Texas (Hybrid/On-site) - Monday-Thursday Onsite, Fridays Remote

About The Company

Our client is a newly established single-family office headquartered in Houston, Texas.

This client is a firm that is focused on achieving “superior alpha generation” with a wide mandate, a long-term investment horizon, and a strategic emphasis on leveraging advanced analytics and AI to drive value across both public and private markets.

Job Summary

The Head of AI Enablement is a principal-level technologist who will serve as both a hands-on practitioner and a visionary leader, building the foundation for growth and owning the company roadmap for data, AI, and technology value creation.

You will own the enterprise data and AI strategy and build a modern analytics platform from the ground up.

In this role, you will define the roadmap to embed advanced analytics and AI capabilities into all aspects of the client's investment workflows, standardize data across diverse business entities, integrate critical systems via APIs, and ensure that leadership and operators have access to timely, trusted insights.

You’ll start as a hands-on builder developing key platform components and proofs of concept, and then evolve into the executive owner of our AI and data capabilities—ultimately leading a team and setting the strategic direction as the function grows.

Key Responsibilities

• Set the vision & roadmap:

• Translate business goals into a pragmatic, phased data and AI strategy and operating model covering data ingestion, modeling, governance, and advanced analytics.

• Outline a multi-year roadmap that aligns technology initiatives with investment objectives and value creation opportunities.

• Build & evolve the platform:

• Lead the architecture and development of the client's internal AI platform , encompassing the Portal, the Vault, and the Engine.

• Ensure this platform is scalable and modular, with strong engineering standards and documentation, enabling new capabilities to be added without re-architecting.

• Integrate critical systems:

• Design and manage integrations/APIs for core business systems (ERP, CRM, HRIS) and external data feeds (e.g. market data providers, SEC/EDGAR filings, economic APIs).

• Build scalable, reliable pipelines to ingest and normalize data (including unstructured documents via document AI techniques) into the Vault, providing a single source of truth ready for analysis and modeling.

• Embed AI into workflows:

• Partner with investment, research, and operations teams to embed AI-driven insights into their daily processes.

• Identify high-impact use cases (e.g. AI-assisted due diligence research such as automated document analysis, portfolio performance analytics, risk scenario simulations) and deliver user-friendly tools – from interactive dashboards to self-service portals – that allow non-technical stakeholders to leverage data and AI insights directly.

• Continuously champion data/AI literacy, training team members and fostering adoption of new analytics capabilities across the firm.

• Stabilize, standardize & secure:

• Establish robust data governance and engineering best practices to bring order to a rapidly evolving analytics environment. Implement processes for data quality control, master data management, and consistent semantic definitions so that insights are fast, accurate, and trustworthy.

• Architect security and compliance into every layer of the platform from day one – enforce strict role-based access controls (RBAC) and permissions, maintain comprehensive data lineage and audit logging for all data and AI outputs, and ensure every critical data point is traceable back to its source.

• Orchestrate resources & partnerships:

• Coordinate with trusted consulting partners and technology vendors to accelerate development of the platform where appropriate. Plan the evolution of internal resources by hiring and mentoring a high-caliber team (e.g. data engineers, ML engineers, and platform developers) as the capabilities mature.

• Foster a culture of excellence within the team, and effectively allocate internal vs. external resources to meet ambitious deliverables and timelines.

• Measure impact & iterate:

• Define key performance indicators to track platform success – for example, system reliability/uptime, pipeline processing times, user adoption rates, and tangible business outcomes such as faster deal evaluations or improved portfolio insights. Rigorously measure and report these results to stakeholders.

• Use data and feedback to iteratively refine the roadmap, ensuring that data and AI initiatives continue to align with business priorities and deliver meaningful value.

Requirements

• Proven technical leadership:

• 10+ years of experience building and owning modern data platforms and AI-driven analytics solutions, with progressively increasing leadership scope.

• Demonstrated ability to operate as a hands-on owner/architect in a fast-paced, ambiguous, multi-entity environment (startup or high-growth context preferred).

• Data integration & pipeline expertise:

• Proven experience integrating diverse systems and data sources via APIs (including ERP/CRM/HRIS systems and third-party data providers) and building data pipelines with enterprise-scale reliability.

• Hands-on expertise in data ingestion and ETL/ELT processes, ensuring data quality and consistency across complex, large-scale datasets.

• Analytics tools & technology skills:

• Deep familiarity with modern data technologies and tools. Hands-on experience with cloud-based data architectures (Microsoft Azure/Microsoft Fabric or similar), data lakehouse/warehouse design, and data engineering frameworks.

• Proficiency with business intelligence and visualization tools (e.g. Power BI or similar) for developing dashboards and enabling self-service analytics.

• AI/ML knowledge:

• Working knowledge of machine learning and AI frameworks relevant to data analytics.

Exposure to Python and its data/ML ecosystem, NLP or document AI techniques, and emerging generative and agentic AI tools (LLMs).

While this is a leadership role, you should be able to understand and guide the application of these technologies in a business setting, identifying opportunities to leverage AI for automation and insight generation.

• Stakeholder management:

• Excellent communication and stakeholder management skills.

• Proven ability to partner with senior executives and cross-functional teams, acting as the “translator” between business needs and technical execution.

• Comfortable explaining complex data or AI concepts in business terms and aligning diverse stakeholders around a common vision.

• Data governance & security:

• Strong knowledge of data governance, privacy, and security best practices.

• Experience implementing role-based access controls, data classification, and audit logging to ensure compliance and trust in analytics outputs.

• A track record of building systems that are secure and auditable by design (e.g. handling sensitive financial data in regulated environments) is highly valued.

• Business acumen:

• Solid understanding of finance and accounting principles, and how operational data and AI-derived insights map to financial performance metrics (“score-keeping”).

• Ability to quickly grasp the client's investment strategies and translate business questions into data/AI solutions, ensuring technology efforts directly support the creation of alpha and efficiency in investment operations.

Preferred Qualifications

• Team building experience:

• Experience standing up a small data/analytics engineering or data science team from scratch, including defining roles, hiring talent, and establishing team processes.

• Familiarity with instituting data governance committees or operating mechanisms in a complex or federated organization is a plus.

• External data familiarity:

• Knowledge of and experience working with third-party financial data sources or subscription datasets (e.g. market data feeds, fund performance databases).

• Hands-on experience blending or harmonizing external data with internal datasets to enrich analysis would be beneficial.

• Emerging AI/analytics innovation:

• Exposure to cutting-edge analytics and AI innovations for productivity or decision support. For example, familiarity with tools like Microsoft Copilot or other generative AI assistants, and a mindset of continuously evaluating new technologies (machine learning algorithms, AI services, etc.) as the data foundation matures.

• Industry domain experience:

• Prior experience in investment management, private equity, or financial services is advantageous.

Understanding the workflows, regulatory considerations, and typical data challenges in the investment domain can help in quickly aligning the platform’s capabilities with the firm’s needs.

Cultural Fit

Our client prides itself on a high-performance, entrepreneurial culture.

The ideal candidate will thrive in a fast-moving, results-driven environment and demonstrate a strong sense of ownership.

Key Expectations Include

• In-office collaboration: Willingness to be on-site in our Houston office Monday–Thursday (approximately 8:30am – 5:30pm) to engage deeply with the team and stakeholders. (Fridays are remote-work days.)

• Entrepreneurial mindset: A proactive, hands-on style and “own it” mentality – willing to roll up your sleeves, collaborate across teams, and adapt to the dynamic, startup-like setting of a newly formed organization.

• Commitment and resilience: Comfortable with a “whatever-it-takes” approach, including being available on occasional weekends or outside standard hours for urgent projects or critical deadlines. You demonstrate resilience under pressure and keep the mission in focus.

Benefits

• Competitive compensation with performance-based incentives.

• Comprehensive health and retirement programs.

• Generous paid time off (PTO).

• Support for professional development and continuous learning.

• Significant influence in shaping the team and technology choices as the function grows (high degree of autonomy and impact).