AI and Data Product Manager

Intercontinental Exchange Holdings, Inc.•Jacksonville, FL

About The Position

Intercontinental Exchange, Inc. (ICE) is seeking an AI and Data Product Manager to lead the transformation of established business workflows by rebuilding them around AI. The role owns the strategy, roadmap, and delivery of AI-powered applications and data products, working across business operations, data science, machine learning engineering, and customers. The position is part of a highly visible team focused on analyzing mortgage and market data and delivering meaningful, responsible, and scalable AI-driven insights to clients. This is primarily a process-transformation role: the manager maps and decomposes workflows, identifies verifiable AI opportunities, and delivers measurable improvements. The role also productizes proprietary data assets, translates AI and machine-learning capabilities into production-grade offerings, establishes evaluation and governance practices, and leads adoption of AI-enabled workflows. The target career tier is mid-level.

Requirements

At least 6 years of product management experience, including demonstrated work building AI/ML-powered products, data products, or workflow-automation solutions.
A demonstrable example of entering an unfamiliar domain, mapping workflows, identifying AI opportunities, and shipping a measurable outcome.
Strong workflow-decomposition skills, including the ability to map processes and assess steps by volume, variance, and verifiability.
Practical understanding of LLMs, RAG, agents, prompt engineering, and evaluation design; coding or model training is not required.
Experience designing evaluations, blind reviews, A/B tests, and acceptance criteria, and iterating based on evidence.
Data literacy, including comfort with SQL and analytics tools to define metrics and inform decisions.
Experience with change management and stakeholder communication to support adoption of AI-driven workflows by non-technical teams.
Ability to recall specific metrics from products shipped, such as hallucination rate, retrieval precision, task success rate, or latency.
Experience working in Agile/Scrum teams and managing a product backlog.
Excellent written and oral communication skills, including the ability to explain probabilistic systems to technical and non-technical audiences.

Nice To Haves

Advanced degree, such as an MBA, or product/Agile certification such as Pragmatic Institute, CSPO, or SAFe POPM.
Hands-on experience with a workflow or process platform such as n8n, Zapier, Make, Workato, Celonis, or UiPath.
Experience launching generative AI/LLM-based or agentic products or features.
Background in operations management, management consulting, analytics/data, or growth/experimentation product management.
Familiarity with cloud platforms such as AWS and modern data warehouses such as Snowflake or Databricks.
Understanding of human-in-the-loop design, model monitoring, drift detection, and responsible-AI frameworks.
Exposure to mortgage technology, capital markets, or financial services; this is helpful but not required.
A computer science degree; the posting notes that demonstrated AI experience is the signal and that such a degree is not required.
Ability to code or train models; the posting states that data literacy and AI comprehension are sufficient.
Prior expertise in mortgage or financial services; the posting states that pattern transfer and rapid domain immersion matter more than industry credentials.

Responsibilities

Map and decompose business workflows end-to-end, identifying high-volume, high-variance, and verifiable steps before determining where AI should be applied.
Redesign processes around AI and prioritize opportunities using volume, variance, and verifiability.
Own product vision, strategy, and multi-quarter roadmaps for AI applications and data products aligned with business objectives.
Assess AI opportunities using conservative, evidence-based ROI assumptions and prioritize tasks whose outputs can be reliably graded.
Partner with data science and ML engineering to turn predictive analytics, NLP, LLM/generative AI, and agentic solutions into reliable, production-grade products.
Define evaluation criteria and acceptance thresholds, establish evals and blind review panels, use LLM-as-judge methods, and monitor production systems for hallucination, bias drift, and model degradation.
Design human-in-the-loop workflows with expert review and expand automation only after each phase is proven.
Turn ICE proprietary data assets into data products, including APIs, data feeds, datasets, dashboards, and embedded analytics.
Write product requirement documents, user stories, and acceptance criteria; maintain and prioritize the product backlog in an Agile/Scrum environment.
Define success metrics and KPIs, including adoption, task success rate, model performance, revenue, and ROI; measure results and improve products continuously.
Lead change management and adoption by bridging data scientists and business owners and helping non-technical stakeholders adopt AI-enabled workflows.
Champion responsible AI with data science, risk, and compliance, including model governance, bias and fairness, explainability, model risk, and data quality.
Ensure products meet relevant regulatory and data-governance requirements for mortgage and financial services, including MISMO, FNMA, FHLMC, GNMA, and applicable privacy standards.
Communicate roadmap, trade-offs, progress, and results to cross-functional partners and executive leadership.