This role is designed to operate ahead of traditional product roadmaps and planning cycles, with a mandate to actively explore how artificial intelligence may disrupt, reshape, or accelerate our business. The role balances strategic thinking with hands on building and experimentation, and is expected to create tangible prototypes, pilots, and proofs of concept in partnership with engineering, platform, and corporate GenAI teams.

The AI Disruptor will examine both defensive risks and offensive opportunities related to client and internal use of AI, while also acting as a creative builder who turns ideas into working demonstrations that can be validated quickly. This is an individual contributor role with broad Capital Access Platforms cross functional influence and high visibility across senior leadership.

Primary Responsibilities

• Identify and explore how AI could materially change client behavior, value propositions, and revenue models across the Data Business.
• Develop and test AI first concepts, workflows, and product ideas through prototypes and pilots rather than static analysis alone.
• Assess defensive risks where AI could bypass, commoditize, or weaken Nasdaq data offerings, and partner with Legal, Compliance, and Product teams to shape practical mitigation approaches.
• Work hands on with Engineering, CAP Platform, and Corporate GenAI teams to build and validate AI enabled capabilities, tools, or agents.
• Translate insights from experiments into clear strategic options, including recommendations on what should be accelerated, redesigned, or deprioritized.
• Identify internal operational use cases where AI can meaningfully improve speed, quality, or scale, and help build small scale solutions to prove impact with strong ROIC.
• Continuously monitor market, competitor, and client AI developments and convert observations into actionable experiments or hypothesis driven builds.

How This Role Operates

• Operates ahead of standard product roadmaps, focusing on future oriented and AI led disruption.
• Combines strategic framing with practical building and experimentation.
• Partners closely with delivery teams but does not own scaled production delivery.
• Works in time bounded cycles that result in tested insights, working demos, or validated recommendations.

Ambition for Role:

• Reimagine how Nasdaq Data is distributed, licensed, consumed, and monetized in an AI-native world by making our proprietary data the default intelligence layer for LLMs, Applications, Agents, Users, Devices, and all Surfaces.
• Build the modern distribution, pricing, entitlement, and product model that allows us to win in MCP, AI marketplaces, closed-LLM environments, and emerging agent ecosystems while protecting our IP, improving customer outcomes, and expanding ARR.
• Drive internal transformation by identifying high-impact operational use cases where AI can materially improve speed, quality, and scale, and rapidly proving value through focused builds with clear ROI.
• Act as the market lens for AI disruption by continuously monitoring competitors, clients, and emerging platforms, converting insights into experiments, prototypes, and scalable opportunities.

Qualifications and Experience

• Demonstrated experience building and experimenting with AI or data driven products, tools, or workflows.
• Strong understanding of how AI impacts data businesses, platforms, or marketplaces.
• Comfort operating in ambiguity and converting loosely defined ideas into working solutions.
• Ability to think strategically while remaining deeply hands on with problem solving and prototyping.
• Proven ability to influence senior stakeholders through clear insights, evidence, and working examples.
• Excellent communication skills, with the ability to explain complex technical or strategic topics simply.

Preferred Attributes

• Builder mindset with demonstrated ability to create from scratch, iterate quickly, and solve ambiguous problems creatively.
• Comfort challenging assumptions and existing models in a constructive manner.
• Experience working across product, technology, and strategy functions.
• Background in regulated or data intensive industries.

KPIs (How Success Will Be Measured)

• Experiment velocity: number of hypotheses tested, prototypes built, or pilots launched per quarter (with clear success criteria defined up front).
• Validation quality: percentage of experiments that produce a decision outcome (advance to delivery team, iterate, or stop) within an agreed timebox.
• Business impact pipeline: number of initiatives that transition from prototype/pilot into funded roadmap work; estimated revenue opportunity protected or created (where measurable).
• Stakeholder adoption and pull-through number of internal teams actively leveraging the demos/agents/tools; qualitative feedback from Product, Engineering, and business leadership.
• Operational uplift: measurable improvements from internal AI use cases (e.g., time saved, cycle time reduction, quality improvement, or cost reduction) validated with partner teams.
• Risk discovery and mitigation: number of material AI-driven threats identified with documented mitigation options (commercial, product, contractual, policy) and leadership alignment on actions.
• Narrative and influence: clarity and usefulness of quarterly readouts (insights, recommendations, and evidence) as assessed by senior leadership; repeatability of the experimentation playbook.

This position offers the opportunity for a hybrid work environment (at least 3 days a week in office, subject to change), providing flexibility and accessibility for qualified candidates.

What We Offer

We're proud to offer a competitive rewards package that is meaningful, recognizes the unique needs of our employees and their families and incentivizes employees for their contribution to Nasdaq's overall success.

The base pay range for this role is $161,000 - $225,000. In addition to base salary, Nasdaq provides a generous annual bonus/commission (short-term incentive), and equity (long-term incentive), comprehensive benefits, and opportunity for growth. Exact compensation may vary based on several job-related factors that are unique to each candidate, including but not limited to: skill set, experience, education/training, business needs and market demands.