Senior Product Manager, AI Experiences

Job Description:

Dow Jones is hiring a Senior Product Manager to help shape how AI shows up across our product portfolio. This role will help build the AI experiences across both consumer products (WSJ, Barron's, MarketWatch) and our professional information products (Factiva, WSJ Pro, Oxford Analytica, Dragonfly). You will partner closely with the internal teams that build our AI agent and orchestration platform, our search platform, and our personalization platform to ensure the experiences you ship consume reusable services rather than one-off implementations. Most importantly, you will represent the end user (whether a subscriber on their morning commute or an analyst building a client briefing) in the AI roadmap to ensure our work represents something that adds value to their lives.

The role sits on the GenAI Experiences team and reports to the Director of Product Management.

• Own the AI experience roadmap for one or more surfaces across the portfolio, with success measured against retention, engagement, and demonstrable user value, not shipped feature count.
• Translate the specific jobs of our users (consumer subscribers, financial analysts, risk and legal professionals, researchers) into AI experiences that do the work, save the time, or reveal insight they could not surface on their own, and ship them on reusable shared capabilities rather than hand-built per product.
• Partner with the internal platform teams to bring your users' requirements into the shared capability roadmap, and run the experimentation and measurement discipline that lets us tell retention-grade stories about AI impact.

• 7+ years of product management experience, preferably with at least 2 years shipping AI-powered products in production (using LLMs, retrieval-augmented generation, agent-based or retrieval-based systems).
• Experience owning a consumer, B2B, or enterprise product that serves a well-defined user, ideally one where retention or engagement is the north-star metric.
• Working fluency in modern AI product patterns: retrieval and context engineering, evaluation frameworks, guardrails, and human-in-the-loop design, plus comfort reasoning about tradeoffs across model, prompt, data, and orchestration layers.

• A track record of shipping on top of shared platforms (internal APIs, SDKs, developer tooling, or third-party AI assistant integrations), with evidence of partnering with platform teams rather than building parallel one-offs.
• Hands-on experience with Model Context Protocol (MCP): has designed or shipped an MCP-based integration for a third-party AI assistant (ChatGPT, Claude, Slack, or similar), including the content-rights and entitlement considerations that distribution through those channels brings.
• Experimentation and measurement discipline: can design an A/B test, define a retention-oriented metric, and gate a release on both business and AI-quality signals.