Head of Data Management & AI

Overview

Marathon Asset Management is a leading global asset manager specializing in public and private credit with ~$23 billion in assets under management. Marathon is recognized as a distinguished leader with 27 years of exceptional performance and partnership. Marathon’s integrated global credit platform is driven by our specialized, highly experienced, and disciplined teams across Private Credit: Direct Lending, Asset Based Lending and Opportunistic Credit and Public Credit: High Yield, Leveraged Loans & CLOs, Emerging Markets, and Structured Credit. Marathon’s mission is to build lasting partnerships with an unwavering commitment to delivering best-in-class performance, service, and reliability on behalf of our clients.

Marathon Asset Management seeks a Head of Data Management & AI to define and execute the firm’s enterprise data, analytics, and artificial intelligence strategy. This role will own the transformation of data and AI into durable strategic digital assets ensuring that core data is accurate, secure, well‑governed, and consumable, and that AI is applied safely and pragmatically to enhance investment decision making, operational performance, risk management, efficiency, and user experience.

This role will serve as the firm’s AI and data visionary, combining deep expertise in data management, data architecture, AI solution design, and product management with strong business acumen. This leader will partner closely with Portfolio Managers, senior executives, and functional leaders to identify, prioritize, and deliver high-impact data, analytics and AI capabilities across investment, operations, risk, finance, and client functions.

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
• Lead modernization initiatives across cloud platforms (Azure, AWS, hybrid architectures) in line with the firm’s Data 360 roadmap
• 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)
• Data cataloging, business glossaries, and critical data element definitions
• Data ownership and stewardship model across key domains (investments, risk, operations, finance, client)
• 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
• Oversee enterprise BI and self-service analytics platforms using tools such as Power BI, Tableau and other AI enabled platforms in use
• 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 Architecture

• Design end-to-end AI lifecycle architecture from experimentation through production, monitoring, and governance
• Architect AI solutions embedded directly into investment and operational workflows
• 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
• Lead product discovery with Portfolio Managers, Analysts, Operations, Risk, and Client teams
• 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

• 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.

• Create an AI and Data Center of Excellence fostering innovation and best practices
• Build and lead a high-performing, virtual organization of data engineers, data scientists, analytics professionals, and product managers, in partnership with internal users and external SMEs
• Establish clear goals, accountability, and performance management frameworks
• Drive AI literacy and maturity programs across the firm
• Manage budget, capacity planning, and resource allocation using a portfolio mindset

• Partner with Investment Teams to enhance research, portfolio construction, and decision-making
• Collaborate with Risk, Operations, Finance, Compliance, Legal, and IT on data and AI-driven capabilities, ensuring control requirements are met
• Support the Customer Success Client Solutions team with AI-enhanced reporting and insights
• Engage external vendors, cloud providers, and research partners

Qualifications

Education

• Bachelor’s degree in Computer Science, Engineering, Data Science, Mathematics, Finance, or related field
• Advanced degree strongly preferred (MS, MBA, Financial Engineering)

Experience

• 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

Technical Expertise

• 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

Preferred Qualifications

• Cloud, AI/ML, or product management certifications
• Background in quantitative finance or fintech innovation
• Strong external industry network

Success Metrics

• Reliability, quality, and efficiency of data platforms and governance program
• User experience, adoption and ROI of governed data products and AI products
• Measurable productivity and decision-quality improvements
• Quality of execution and speed from concept to production
• Strength, engagement, and maturity of the data and AI organization

The average salary for this role $300,000 in base pay and is exclusive of any bonuses or benefits. The base pay offered will be determined based on your experience, location, skills, training, certifications and education, and in addition we will also consider internal equity and market data. We do not anticipate that candidates hired will begin at the top of the range however, from time to time, it may occur on a case-by-case basis.

No agencies please.

Equal Opportunity Employer M/F/D/V