Head of Data Management & AI

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. Marathons 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. Marathons 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 firms 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 wellgoverned 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 firms 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.

1. Enterprise Data & AI Strategy (Executive Leadership)

• 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

1. Enterprise Data Architecture & Platform (Foundation)

• Architect and oversee the firms 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 firms 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

1. Advanced Analytics & AI Capabilities (Business Impact)

• 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.

1. AI Solution Architecture & Product Management

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

1. 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 dataquality standards and controls; establish monitoring dashboards and remediation workflows for highvalue domains.
• Own enterprise data KPIs/OKRs (dataquality 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 datagovernance requirements in design testing and golive criteria.
• Partner with Risk Compliance and Internal Audit on datarelated issues findings and remediation plans.

1. Team Leadership & Organizational Development

• 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

1. Cross-Functional Partnership

• 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

Education

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

Experience

• 1215 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 $300000 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