Manager, Product Owner - AI/ML & Market Insights
$81.1k - $135.1kCME Group
We are seeking a highly collaborative and commercially driven AI/ML and Market Insights Product Owner to drive the development of machine learning and generative AI solutions across our derivatives exchange ecosystem. Sitting at the center of innovation, you will bridge the gap between business strategy and advanced data science, translating diverse departmental needs into a unified, high-impact AI roadmap. In this role, you will focus entirely on the "what" and the "why," empowering our highly skilled technical Pod to handle the "how." You will thrive here if you are an exceptional communicator, passionate about financial markets, and excel at translating technical progress into clear, actionable business value. We are looking for a collaborative bridge, who excels at moving complex AI initiatives forward to optimize exchange liquidity, automate market research, supercharge sales intelligence, and elevate our marketing automation. Key Responsibilities Cross-Functional Collaboration & Product Roadmap
- Stakeholder Engagement: Spend the vast majority of your time meeting directly with internal stakeholders and business leaders to align expectations, communicate progress, and translate vague business goals into clear, actionable requirements.
- Sales & Business Lines: Partner with leadership to build predictive client-intelligence models, identify cross-selling opportunities, and forecast volume trends.
- Quantitative Research & Market Intelligence: Collaborate with researchers to productize advanced liquidity insights (such as order book depth, bid-ask spread dynamics, and order flow), volatility modeling, and market-microstructure analytics into scalable AI features.
- Marketing: Work with marketing teams to deploy generative AI tools for automated market commentary, personalized client campaigns, and predictive lead scoring.
- Technology Leadership: Act as the central bridge to platform engineering, bringing engineering leaders into stakeholder discussions early to analyze technical feasibility and ensure strategic alignment.
- Data Engineering: Collaborate to ensure the data pipelines feeding your models (e.g., historical tick data, order flow) meet strict latency, quality, and production standards.
- Backlog Management: Own, refine, and prioritize the team backlog using value-driven frameworks (e.g., WSJF) to ensure the pod is always working on the highest-impact deliverables.
- Feature Decomposition: Break down complex AI/ML features into clear, manageable user stories with precise acceptance criteria.
- Agile Ceremonies: Lead core Agile ceremonies for your Pod, including Sprint Planning, Backlog Refinement, Sprint Reviews, and Retrospectives.
- PI Planning: Participate actively in Program Increment (PI) Planning, drafting team PI objectives and identifying cross-pod dependencies within the Agile Release Train.
- Workflow Management: Help maintain the team's focus by managing ad-hoc requests, and aligning new scope with prioritized backlog goals so the data science team can execute effectively.
- Performance Tracking: Define and track metrics for both model performance (accuracy, precision, recall) and business impact (adoption, revenue, market efficiency).
- Compliance & Transparency: Ensure that AI outputs are transparent, explainable, and fully compliant with strict financial regulatory standards.
- Education: Bachelor's degree required (Business, Computer Science, Data Science, Product Management, or related field).
- Experience: 3+ years as a Product Manager/Owner, ideally delivering technical or AI/ML solutions within capital markets, derivatives exchanges, or fintech.
- Domain Knowledge: Foundational understanding of financial derivatives (futures, options), market liquidity, and order book mechanics.
- Agile/SAFe Fluency: Strong understanding of Agile methodologies. Experience operating within a SAFe environment is highly preferred (SAFe POPM or CSPO certifications are a strong plus).
- ML Lifecycle: Strong understanding of the end-to-end ML lifecycle, including data collection, model training, deployment, and drift monitoring.
- Data Literacy: Proficient in querying data (e.g., SQL basics) to validate assumptions, investigate issues, and drive data-informed decisions.
- AI/ML Certification: Accredited AI/ML credential strongly recommended upon application and required shortly after hire (e.g., Google Cloud PMLE, Generative AI Leader, Google Cloud Skill Badges, or Google AI Essentials).
- Technical Translator: Exceptional ability to bridge technical data science teams and business stakeholders, translating complex models into business value and commercial goals into precise technical requirements.
- Stakeholder Mastery: Collaborative, team-oriented approach with a track record of successfully balancing competing priorities across diverse stakeholders.
Vacancy posted 3 days ago
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