Director of Data & AI
Role details
Job location
Tech stack
Job description
We are looking for a high-impact Head of Data & AI to serve as our lead architect and strategist for AI adoption as well as lead our Data and Insights team. This is a senior role for an AI leader who understands that an AI strategy is only as good as the data infrastructure supporting it. You will be the bridge between our raw data silos and actionable AI implementation, working cross-functionally to prepare our environment for enterprise-scale AI. This role requires a leader who can define a high-level AI strategy, manage the tactical roadmap, and roll up their sleeves to oversee the operational deployment of AI-driven products and processes., * AI Opportunity Mapping: Identify and prioritize high-impact areas across the business where AI can drive revenue or efficiency. Design the roadmap and bring to implementation while partnering with the Business Lines/Functional AI champions
- Executive Advisory: Act as the primary subject matter expert for the leadership team on AI trends, risks, and competitive advantages.
- Governance & Ethics: Develop frameworks for responsible AI use, ensuring data privacy, transparency, and compliance with emerging regulations.
Tactical (Planning & Architecture)
- Portfolio Management: Manage a pipeline of AI projects from "Proof of Concept" (PoC) to full-scale production.
- Cross-Functional Collaboration: Be the sparring partner to the Business Leads and AI champions on the AI efficiencies and implementation in the business context. Partner with Product, Engineering, and Data teams to ensure AI initiatives are technically feasible and aligned with the core product.
- Data Readiness Audit: Evaluate and map disparate data sources across the organization to determine "AI-readiness" (quality, labeling, and accessibility).
- Infrastructure Design: Architect the "AI Pipeline"-identifying the necessary data sources, vector databases, and MLOps tools needed to move from local pilots to production.
Operational (Execution & Delivery)
- Lead AI Implementation: Oversee the day-to-day progress of AI developments, removing blockers and ensuring high-quality output.
- Performance Monitoring: Define and track KPIs for all deployed models.
- Organizational Upskilling: Partner with People and Culture on the Organizational upskilling program to ensure the AI adoption is broad, including internal workshops and training to upskill non-technical staff on how to use AI tools effectively in their daily workflows. In collaboration, design dedicated online education channels and measure the adoption.
- Stakeholder Navigation: Act as the primary liaison between technical teams (IT, Engineering) and business units and support functions Legal, Marketing, People and Culture and other, to align AI goals.
- Change Advocacy: Educate and influence senior leadership on the reality of AI timelines, specifically managing expectations regarding data cleaning and infrastructure setup. Bring in the positive change in the changing jobs scoping and efficiencies driven by AI implementation.
- ROI Modeling: Work with finance and department heads to quantify the impact of AI initiatives on the bottom line.
Leadership
- Team Leadership & Mentorship: Direct management of the Data & Insights team, transitioning them from traditional reporting/BI to an "AI-first" mindset through active coaching and technical upskilling.
- Data Lifecycle Ownership: Oversee the end-to-end data pipeline-from ingestion and cleaning to advanced analytics-ensuring the team delivers high-quality datasets that serve as the foundation for organizational strategy.
- Modernizing the Stack: Lead the evolution of the team's toolkit, moving from static dashboards to predictive insights and automated data delivery systems.
- Performance & Impact Culture: Define and implement rigorous standards for data accuracy and project delivery, ensuring the team's output directly correlates to business KPIs and strategic decision-making.
- Cross-Pollination of Expertise: Facilitate collaboration between the Data team and other business units to ensure insights are not siloed, but are instead integrated into the operational workflows of the entire organization.
Requirements
Do you have experience in Leadership?, * Experience: 8+ years in technology leadership, with at least 3+ years specifically focused on implementing Machine Learning or Generative AI solutions.
- Technical Fluency: Deep understanding of the AI lifecycle (data preparation, fine-tuning, RAG architecture, and deployment). While you may not code daily, you can conduct a technical review of an architecture diagram.
- Data Engineering Fluency: Deep knowledge of data warehousing, ETL/ELT processes, and data modeling.
- Business Acumen: Proven ability to translate complex technical concepts into "business speak" for stakeholders and clients.
- Pragmatism: A "product-first" mindset-you value a simple solution that works today over a perfect model that takes a year to build.