Head of Data Management & AI

Marathon Asset Management, LP•New York, NY

1d•$300,000

About The Position

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.

Requirements

Bachelor’s degree in Computer Science, Engineering, Data Science, Mathematics, Finance, or related field
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

Nice To Haves

Advanced degree strongly preferred (MS, MBA, Financial Engineering)
Cloud, AI/ML, or product management certifications
Background in quantitative finance or fintech innovation
Strong external industry network

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.
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)
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