Product Manager II, AI

About the Role

Key Responsibilities

• Partner with Engineering, Data Science, and domain leaders to surface high-impact automation and decision-support use cases and sequence them into a staged roadmap that accelerates DraftKings' AI-transformation goals.
• Deliver SDKs, templates, and documentation so any DraftKings team can spin up compliant, observable AI agents without reinventing the wheel.
• Gather requirements from operators, analysts, product managers, and marketers; convert them into clear platform capabilities, agent behaviors, API contracts, and success metrics.
• Drive squads through PoCs, model selection, evaluation, A/B testing, and production deployment-championing responsible-AI practices every step of the way.
• Define and own KPIs such as time-to-insight, workflow-automation %, cost savings, and agent adoption; build dashboards that reveal what's working (and what's not).
• Partner with Security, Legal, and Compliance on data privacy, bias detection, and model-governance policies so innovation never outruns our responsibilities.
• Host demos and craft playbooks that raise AI literacy company-wide and ensure new capabilities land with maximum impact.

Requirements

• 3+ years of product management (or equivalent) delivering data- or platform-centric products from concept through broad adoption.
• Demonstrated success shipping ML- or generative-AI-powered solutions that improve internal workflows.
• Ability to translate technical concepts (model performance, latency, scalability) into language understood by business stakeholders-and vice versa.
• Hands-on familiarity with A/B testing, experimentation design, and basic statistical analysis.
• Agile mindset with strong prioritization skills and a track record of moving fast in highly collaborative environments.
• Clear, persuasive communication that rallies executives and front-line users around a shared AI vision.
• Experience in regulated domains such as sports betting, fintech, or health.
• Exposure to LLM architectures, vector databases, orchestration frameworks (LangChain/LlamaIndex), or similar technologies.
• Familiarity with MLOps tooling (Databricks, feature stores, Kubernetes) and ML observability practices.
• Background in causal inference or more advanced statistical techniques.

Nice to Have

• Exposure to LLM architectures, vector databases, orchestration frameworks (LangChain/LlamaIndex), or similar technologies.
• Familiarity with MLOps tooling (Databricks, feature stores, Kubernetes) and ML observability practices.
• Background in causal inference or more advanced statistical techniques.