Vice President, Product, Data & AI

Over the last 20 years, Ares’ success has been driven by our people and our culture. Today, our team is guided by our core values – Collaborative, Responsible, Entrepreneurial, Self-Aware, Trustworthy – and our purpose to be a catalyst for shared prosperity and a better future. Through our recruitment, career development and employee-focused programming, we are committed to fostering a welcoming and inclusive work environment where high-performance talent of diverse backgrounds, experiences, and perspectives can build careers within this exciting and growing industry.

Job Description

The VP, Product: Data and AI is a senior product leader within the central hub, responsible for defining what the firm builds with AI and why — translating the needs of investment professionals, operations teams, IR, legal, and compliance into a coherent AI use-case portfolio that delivers measurable business value.

This role owns the AI use-case roadmap, the intake-to-delivery lifecycle, and the AI governance gate process that moves use cases from concept through Legal, Compliance, Risk, and Cyber sign-off into production. The VP serves as the connective tissue between business stakeholders who surface problems, the vertical technology teams, and AI Engineering hub that builds the platform to solve them.

This is a strategic and execution-oriented role in equal measure. You will partner with vertical PM tech teams and run discovery with deal teams, IR, and operations; write crisp product specs that engineering can execute against; define success metrics; and manage the use-case funnel across investment and corporate functions.

The AI Product Management function owns the demand side of the AI platform: what gets built, in what order, and with what definition of success. Vertical spoke teams in each investment vertical surface use-case demand; the VP, AI Product Management qualifies, prioritizes, and shepherds the most valuable use cases through delivery as centrally-built platform capabilities, applying the two-vertical rule as the primary governance lens for hub vs. spoke build decisions.

Key Responsibilities

AI Use-Case Strategy & Roadmap

• Own the enterprise AI use-case roadmap across all firm functions — deal execution, portfolio operations, investor relations, legal & compliance, and firm-wide productivity — with prioritization aligned to business leadership
• Apply the two-vertical rule as the governing framework: when a use case is applicable across two or more verticals or functions, lead the business case for centralizing on the hub platform rather than rebuilding in each spoke
• Define and maintain the AI use-case intake funnel: structured discovery, feasibility scoring, value estimation, and sequencing logic that balances strategic impact with engineering capacity
• Translate high-level business goals (analyst time savings, decision support, operational efficiency) into a portfolio of AI initiatives with clear owners, milestones, and success criteria
• Stay current on generative AI and agentic capabilities; proactively identify where emerging platform primitives (MCP integrations, A2A workflows, new model capabilities) unlock net-new use cases for the firm

Discovery, Scoping & Requirements

• Lead structured discovery sessions with deal teams, portfolio operations, IR, legal, compliance, and senior stakeholders to surface high-value AI opportunities and translate them into product requirements
• Produce well-structured product specifications: user stories, workflow diagrams, acceptance criteria, context layer definitions (Firm/Deal/User), retrieval scope, and output format requirements — written to the standard AI Engineering executes against
• Distinguish between use cases suited for RAG-based retrieval, agentic orchestration, structured extraction, or analytical AI — and articulate the distinction clearly to both technical and business audiences
• Own MNPI sensitivity classification for each use case; partner with Data Governance, Legal and Compliance during scoping to determine information barrier requirements before engineering engagement
• For portfolio operations and reporting use cases, collaborate with Data Product Management to ensure Gold-layer data products required for AI retrieval are defined and on roadmap before engineering begins

AI Governance Gate & Compliance

• Own the AI governance gate process end-to-end: producing the use-case submission package for Legal, Compliance, Risk, and Cyber sign-off and driving each use case through the gate to approved status
• Support the governance submission template and ensure all AI use cases — regardless of vertical or function — follow a consistent review process before production deployment
• Manage ongoing governance obligations post-deployment: monitoring thresholds, model change notifications, and periodic reviews required by compliance stakeholders
• Partner with Data Governance on AI-specific policy: retrieval permissioning, audit log requirements, acceptable use definitions, and model output disclaimers
• Serve as the business-side point of contact for ARB (Architecture Review Board) submissions on AI use cases, coordinating with AI Engineering on technical evidence packages

Delivery Partnership & Use-Case Lifecycle

• Partner with the Principal AI Engineer and AI Engineering team through the full use-case lifecycle: from approved spec through build, evaluation, staged rollout, and production launch
• Define evaluation criteria and user acceptance tests for each use case — retrieval quality benchmarks, output format adherence, latency thresholds, and qualitative review with business stakeholders
• Own the feedback loop post-launch: structured user feedback collection, monitoring of observability metrics, and iteration prioritization with AI Engineering
• Track time-to-value for each use case from intake to production; report use-case portfolio status and business impact to business leadership on a regular cadence
• Manage the transition of use cases from spoke-built prototypes to hub platform capabilities, coordinating change management with vertical teams and their stakeholders

Stakeholder Engagement & Change Management

• Build and maintain trusted relationships with senior stakeholders across investment verticals —deal professionals, operations, IR, and Legal and Compliance — as the face of the AI product function
• Run structured demos, pilots, and feedback sessions with business users; translate qualitative feedback into actionable product decisions and communicate decisions back to stakeholders with clear rationale
• Develop and execute change management plans for AI use-case launches, including user training, adoption tracking, and escalation pathways for issues
• Partner with the Data & AI PMO on cross-functional coordination, sprint ceremonies, and executive reporting across the AI use-case portfolio
• Champion responsible AI use within the firm: communicating model limitations, output uncertainty, and appropriate human-in-the-loop requirements to business users

Product & Domain Experience

• 8+ years in product management; 3+ years in an AI, ML, or data product role with direct ownership of LLM or generative AI use cases from discovery through production
• Demonstrated experience translating complex, knowledge-intensive workflows (legal review, research, financial analysis, report generation) into AI product requirements that engineering teams can execute against
• Track record of managing multi-stakeholder use-case portfolios with competing priorities across business functions — not just a single product line
• Experience operating in or closely with regulated industries (financial services, legal, healthcare): familiar with governance gate processes, compliance review requirements, and the discipline of shipping AI responsibly
• Strong understanding of RAG architectures, agentic workflows, prompt design tradeoffs, and retrieval quality concepts — sufficient to write precise requirements and hold technical conversations with AI Engineering

Skills & Competencies

• Exceptional written communication: able to produce crisp product specs, executive memos, board-ready summaries, and governance submission packages with equal fluency
• Strong analytical reasoning: able to build business cases, quantify expected value (time savings, risk reduction, revenue support), and defend prioritization decisions with data
• Highly organized with strong program management instincts — able to run a multi-use-case intake funnel, track parallel workstreams, and keep stakeholders aligned without losing velocity
• Comfortable operating at both the strategic level (roadmap, prioritization, executive alignment) and the tactical level (writing acceptance criteria, reviewing retrieval results, coordinating rollout)
• High accountability: takes ownership of outcomes, communicates blockers early, and builds trust with both technical and business partners

Preferred Qualifications

• Private equity, investment banking, asset management experience — familiarity with deal workflows, IC memo process, fund reporting cycles, LP communications, or portfolio monitoring
• Experience managing AI governance or responsible AI review processes in a regulated environment, including interactions with Legal, Compliance, or Risk functions
• Familiarity with Databricks, Unity Catalog, or enterprise data platforms — sufficient to understand how data product maturity constrains AI use-case delivery
• Experience with LLM evaluation frameworks (e.g., RAGAS, LLM-as-judge, human evaluation rubrics) and the ability to define measurable acceptance criteria for generative AI outputs
• Prior experience in a hub-and-spoke or federated operating model — comfortable defining central platform policy while enabling vertical teams to move with autonomy
• MBA or advanced degree in a relevant field (finance, computer science, information systems) is a plus but not required

Reporting Relationships

Compensation

$225,000 - $275,000

There is no set deadline to apply for this job opportunity. Applications will be accepted on an ongoing basis until the search is no longer active.