Head of Data Management & AI

Responsibilities

• Define and execute the firm-wide data, analytics, and AI roadmap aligned with business objectives and investment strategy
• Act as a trusted advisor to senior leadership on data and AI opportunities, risks, and investments
• Champion a data-driven and AI-enabled culture through education, communication, and demonstrable business impact
• Lead data and AI governance, including data ownership and stewardship, model risk management, explainability, bias mitigation, and regulatory compliance
• Develop business cases and ROI models for major data and AI initiatives, linking them to measurable improvements in decision quality, control and efficiency
• Evaluate emerging technologies (LLMs, generative AI, agents, alternative data) and determine strategic applicability on top of a trusted data foundation
• Own build vs. buy vs. partner decisions for data and AI platforms, tooling, and vendors
• Architect and oversee the firm’s enterprise data platform, including data lakes, warehouses, analytics environments, and feature stores
• Build AI-ready and governance-ready data infrastructure with versioned datasets, robust data quality controls, lineage, and metadata management
• Design scalable data pipelines integrating market data, alternative data, trading systems, portfolio platforms, custodians, fund administrators, and proprietary systems
• Establish enterprise data governance: Data policies and standards (classification, retention, access, quality, lineage)
• Implement metadata, lineage, and data-quality tooling and embed them into development, testing and release processes
• Ensure data platform security, resilience, performance, and cost efficiency, including role-based access, privacy compliance (GDPR, CCPA), and regular access reviews
• Drive API and integration strategy to enable secure, governed data and AI access across the firm
• Direct development of analytics and AI solutions supporting various Credit business and operations functions
• Enable AI-powered: Investment research synthesis and reporting Client communications and investor materials Internal knowledge management and Q&A systems Regulatory and client reporting automation
• Partner with the investment team to productionize models and back-testing frameworks for quantitative and systematic strategies, with clear data-lineage and control evidence.
• AI Solution Architecture & Product Management
• AI Architecture Design end-to-end AI lifecycle architecture from experimentation through production, monitoring, and governance
• Implement model monitoring, observability, explainability, and security controls
• Design secure model deployment patterns (batch and real-time)
• AI Product Management
• Manage AI as a product portfolio with clear vision, roadmaps, and success metrics
• Prioritize initiatives based on business value, feasibility, data readiness, and strategic alignment
• Own adoption, change management, training, and continuous improvement
• Measure performance via usage, accuracy, satisfaction, and ROI metrics
• Data Governance, Quality and Operating Model Design and run the Data Governance Operating Model, including steering committees, data councils and working groups
• Define, publish, and enforce data‑quality standards and controls; establish monitoring, dashboards, and remediation workflows for high‑value domains
• Own enterprise data KPIs/OKRs (data‑quality scores, lineage coverage, access review completion, incident resolution times, usage of certified data products) and report regularly to senior leadership
• Ensure new initiatives (systems, products, regulatory changes, AI use cases) incorporate data‑governance requirements in design, testing, and go‑live criteria
• Partner with Risk, Compliance, and Internal Audit on data‑related issues, findings, and remediation plans

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Data Science, Mathematics, Finance, or related field
• Advanced degree strongly preferred (MS, MBA, Financial Engineering)
• 12–15+ years in data, analytics, and technology leadership
• 8+ years in financial services (asset management, hedge funds, investment banking)
• Proven success delivering enterprise-scale data and AI platforms and solutions
• Experience partnering with executive leadership and investment professionals
• Track record leading large-scale platform, governance and capability transformations
• Modern data architecture and cloud platforms (Snowflake, Databricks, Fabric, Azure)
• Python, SQL, ML frameworks, and feature engineering pipelines
• LLMs, RAG, agent frameworks, and AI orchestration platforms
• MLOps / LLMOps, model deployment, monitoring, and governance
• BI and visualization platforms (Power BI, Tableau)
• Financial market data, portfolio systems, and regulatory reporting