AI and Data Product Manager

AI And Data Product Manager

Intercontinental Exchange, Inc. (ICE) is seeking an AI and Data Product Manager to lead the transformation of established business workflows by rebuilding them around AInot 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 verifiablenot 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 include:

• Mapping and decomposing existing business workflows end-to-endidentifying steps that are high-volume, high-variance, and verifiablebefore deciding where AI belongs.
• Reimagining processes around AI rather than bolting AI onto current steps, prioritizing opportunities by the principle of volume, variance, and verifiability.
• Defining and owning the product vision, strategy, and multi-quarter roadmap for a portfolio of AI applications and data products aligned to business objectives.
• Sizing AI opportunities with conservative, evidence-based ROI assumptions, targeting tasks whose outputs can be reliably graded and avoiding "too much, too fast" over-commitment.
• Partnering closely with data science and ML engineering to translate modelsincluding predictive analytics, NLP, and LLM/generative AI and agentic solutionsinto reliable, production-grade products.
• Designing evaluation criteria and acceptance thresholds ("define good before building"); establishing evals, blind review panels, and LLM-as-judge methods, and monitoring for hallucination, bias drift, and model degradation in production.
• Architecting human-in-the-loop workflows with expert review designed in, expanding automation only after each phase is proven.
• Productizing ICE's proprietary data assets into well-defined data products such as APIs, data feeds, datasets, dashboards, and embedded analytics.
• Writing clear product requirement documents (PRDs), user stories, and acceptance criteria; maintaining and prioritizing the product backlog within an Agile/Scrum environment.
• Defining success metrics and KPIs (adoption, task success rate, model performance, revenue, ROI) and using data to measure outcomes and continuously improve products.
• Driving change management and adoptionbridging data scientists and business owners and getting non-technical stakeholders to embrace AI-changed workflows.
• Championing responsible AI in partnership with data science, risk, and compliance: model governance, bias and fairness, explainability, model risk, and data quality.
• Ensuring products meet regulatory and data-governance requirements relevant to mortgage and financial services (e.g., MISMO, FNMA, FHLMC, GNMA, and applicable privacy standards).
• Communicating roadmap, trade-offs, progress, and results to cross-functional partners and executive leadership.

Knowledge and experience required includes:

• 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 measurableindustry 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 includes:

• 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 platforme.g., n8n, Zapier, Make, Workato, Celonis, UiPath.
• Experience launching generative AI / LLM-based or agentic products or features.
• Background that develops process thinking before AIoperations 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 includes:

• 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.

Intercontinental Exchange, Inc. is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to legally protected characteristics.