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Job Purpose

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-not by layering AI on top of them. This role owns the strategy, roadmap, and delivery of our AI-powered applications and data products, sitting at the intersection of business operations, data science, machine learning engineering, and our customers. You will be part of a highly visible team central to ICE's strategy to analyze mortgage and market data and deliver AI-driven insights to our clients in a meaningful, responsible, and scalable way.

This is a process-transformation role first and a feature-delivery role second. We hire for the ability to decompose workflows and apply AI where it is verifiable-not for prior expertise in any specific industry. A track record of entering an unfamiliar domain, mapping its workflows, and shipping something measurable is the signal we value most.

Responsibilities

• Map and decompose existing business workflows end-to-end-identifying steps that are high-volume, high-variance, and verifiable-before deciding where AI belongs.

• Reimagine processes around AI rather than bolting AI onto current steps, prioritizing opportunities by the principle of volume, variance, and verifiability.

• Define and own the product vision, strategy, and multi-quarter roadmap for a portfolio of AI applications and data products aligned to business objectives.

• Size AI opportunities with conservative, evidence-based ROI assumptions, targeting tasks whose outputs can be reliably graded and avoiding "too much, too fast" over-commitment.

• Partner closely with data science and ML engineering to translate models-including predictive analytics, NLP, and LLM/generative AI and agentic solutions-into reliable, production-grade products.

• Design evaluation criteria and acceptance thresholds ("define good before building"); establish evals, blind review panels, and LLM-as-judge methods, and monitor for hallucination, bias drift, and model degradation in production.

• Architect human-in-the-loop workflows with expert review designed in, expanding automation only after each phase is proven.

• Productize ICE's proprietary data assets into well-defined data products such as APIs, data feeds, datasets, dashboards, and embedded analytics.

• Write clear product requirement documents (PRDs), user stories, and acceptance criteria; maintain and prioritize the product backlog within an Agile/Scrum environment.

• Define success metrics and KPIs (adoption, task success rate, model performance, revenue, ROI) and use data to measure outcomes and continuously improve products.

• Drive change management and adoption-bridging data scientists and business owners and getting non-technical stakeholders to embrace AI-changed workflows.

• Champion responsible AI in partnership with data science, risk, and compliance: model governance, bias and fairness, explainability, model risk, and data quality.

• Ensure products meet regulatory and data-governance requirements relevant to mortgage and financial services (e.g., MISMO, FNMA, FHLMC, GNMA, and applicable privacy standards).

• Communicate roadmap, trade-offs, progress, and results to cross-functional partners and executive leadership.

Knowledge and Experience

• 6+ years of product management experience, with demonstrated work building AI/ML-powered products, data products, or workflow-automation solutions (mid-level is the target tier).

• A demonstrable example of entering a domain cold, mapping its workflows, identifying AI leverage points, and shipping something measurable-industry independent.

• Strong process-decomposition skills: the ability to map a workflow in detail and score steps by volume, variance, and verifiability.

• Practical AI literacy: working comprehension of LLMs, RAG, agents, prompt engineering, and evaluation design (you do not need to code or train models).

• Empirical mindset: experience designing evals, blind reviews, A/B tests, and acceptance criteria, and iterating against evidence.

• Data literacy, including comfort with SQL and analytics tools to define metrics and inform decisions.

• Change-management and stakeholder-translation experience getting non-technical teams to adopt new, AI-driven ways of working.

• Ability to recall specific metrics from products you have shipped (e.g., hallucination rate, retrieval precision, task success rate, latency).

• Proven experience working in Agile/Scrum teams and managing a product backlog.

• Excellent written and oral communication, with the ability to explain probabilistic systems to both technical and non-technical audiences.

Preferred Knowledge and Experience

• Advanced degree (e.g., MBA) or product/Agile certification (e.g., Pragmatic Institute, CSPO, SAFe POPM).

• Hands-on experience with at least one workflow or process platform-e.g., n8n, Zapier, Make, Workato, Celonis, UiPath.

• Experience launching generative AI / LLM-based or agentic products or features.

• Background that develops process thinking before AI-operations management, management consulting, analytics/data, or growth/experimentation product management.

• Familiarity with cloud platforms (e.g., 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 is helpful but not required.

• A computer science degree. Roughly 60% of working AI PMs do not hold one; demonstrated AI experience is the signal.

• The ability to code or train models. Data literacy and AI comprehension are sufficient.

• Prior expertise in mortgage or financial services. Pattern transfer and rapid domain immersion matter more than industry credentials; domain knowledge can be borrowed empirically from practitioners.

Technical & Tool Familiarity

• AI/ML Concepts: LLMs, RAG, agents, prompt engineering, and evaluation metrics (precision, recall, F1, hallucination rate, task success rate, latency).

• AI Orchestration (Execution): UiPath Maestro, n8n; awareness of agent protocols such as MCP, A2A, and ACP.

• Workflow Builders (Prototyping): Any of n8n, Zapier, Make, Workato.

• Data Platforms: SQL, Snowflake, Databricks, Spark; data pipelines and ETL concepts.

• Cloud: AWS (or comparable cloud environments).

• Visualization & BI: SIGMA, Tableau, Microsoft Power BI.

• Product & Delivery: Jira, Confluence, Productboard; product analytics.