Director of Data Strategy & Solutions, PulsePoint

• This isn’t a role for someone who executes against a pre-defined plan. This is a role for someone who writes the plan, challenges assumptions, and sees clearly where data strategy and AI are converging and builds toward that future with urgency
• We want a leader who doesn’t just understand that shift but is actively living it. Someone for whom AI is native, not adjacent
• 7 to 10 years of relevant experience in data strategy, analytics, or data product development
• Demonstrated experience working at the intersection of data and AI, whether through AI-powered product development, AI vendor evaluation, or building AI-augmented analytics workflows
• Technical Data Partnership experience, including evaluating and onboarding data vendors with an understanding of how AI is reshaping what those partnerships look like
• Strong SQL skills and comfort working directly with complex, real-world data sets
• Hands-on experience with data analysis and manipulation, not just directing others to do it
• Ability to construct and communicate a strategic point of view clearly, in writing and in a room

Desirable

• We’re looking for a senior, opinionated thought leader to serve as Director of Data Strategy & Solutions. Someone who will own and drive PulsePoint’s data strategy roadmap, forge the data partnerships that power our platform, and envision the next generation of AI-driven data products.
• We’re at an inflection point. The way data is sourced, processed, interpreted and activated is being fundamentally reshaped by AI
• Specifically, here are some exciting things you’ll be working on:
• Own and evolve PulsePoint’s data strategy roadmap with an AI-first lens, identifying where agentic AI, large language models, and machine learning can replace, augment or accelerate our existing data capabilities
• Lead the evaluation and adoption of AI-powered data solutions, from synthetic data generation and AI-driven identity resolution to LLM-based insights and autonomous analytics agents
• Scout and assess AI and data partnership opportunities, including emerging vendors in the AI/ML space, and determine how they fit into our data ecosystem strategically and technically
• Prototype and prove out new AI-augmented data products and capabilities, moving quickly from hypothesis to working concept with the tools available today
• Develop and champion a point of view on how agentic AI workflows will change the way our teams and clients interact with data, and translate that vision into a concrete roadmap
• Present data strategy recommendations and AI investment cases to senior stakeholders and decision-makers in a way that is clear, credible and compelling
• Act as an internal authority on the intersection of AI and data, upskilling teams, shaping how we evaluate new tools, and setting standards for responsible AI use in our data practice
• Synthesize signals from multiple data sources into strategic insights, using AI-assisted analysis to surface trends faster and communicate findings with clarity
• Own the quality and integrity of our data assets, establishing monitoring frameworks that increasingly leverage automation and AI to detect anomalies and drive proactive resolution
• Evaluate data partners not just for the data they provide but for their AI capabilities, data infrastructure maturity, and ability to evolve alongside us
• Technical Data Partnerships: Evaluate potential data and AI partners through both a strategic and technical lens. Develop and manage onboarding workflows, success measurement, and rollout of new data solutions. Manage ongoing relationships with an eye toward how partners are evolving their AI capabilities
• Data Quality & Automation: Own quality evaluation and monitoring, with a mandate to progressively automate quality checks using AI-driven tooling. Support sales and RFPs, drive proactive analysis, and roll out scalable processes that reduce manual effort over time
• Data Architecture & Governance: Own taxonomies, data governance frameworks, and solutions architecture. Ensure our data infrastructure is AI-ready, well-documented, and built to support both current needs and the agentic workflows that are coming
• AI-Native Data Strategy: Partner with Product, BD, and Tech to define where AI creates the most leverage in our data products and platform. Own the data strategy roadmap and push the organization toward solutions that are scalable, intelligent, and built for where the industry is heading, not just where it is today
• Analytics & Hypothesis-Driven Thinking: Lead the analytical process that takes teams from hypothesis to validated insight, directing the assembly of the right data, the right models, and the right AI tools to confirm or reject assumptions quickly and confidently