Data Analyst, Product Strategy & AI

About AlphaSense:  The world’s most sophisticated companies rely on AlphaSense to remove uncertainty from decision-making. With market intelligence and search built on proven AI, AlphaSense delivers insights that matter from content you can trust. Our universe of public and private content includes equity research, company filings, event transcripts, expert calls, news, trade journals, and clients’ own research content. The acquisition of Tegus by AlphaSense in 2024 advances our shared mission to empower professionals to make smarter decisions through AI-driven market intelligence. Together, AlphaSense and Tegus will accelerate growth, innovation, and content expansion, with complementary product and content capabilities that enable users to unearth even more comprehensive insights from thousands of content sets. Our platform is trusted by over 6,000 enterprise customers, including a majority of the S&P 500. Founded in 2011, AlphaSense is headquartered in New York City with more than 2,000 employees across the globe and offices in the U.S., U.K., Finland, India, Singapore, Canada, and Ireland. Come join us! About the Role:  AlphaSense is seeking a highly analytical, entrepreneurial Data Analyst, Product Strategy & AI to serve as the analytical engine for our Product Management team and own our most important product analytics questions. Our foundational data engineering and reporting are expertly managed by our technical team in India. We are hiring this NYC-based role to be the "connective tissue" between our raw data and our strategic product decisions. You will not be spending your days building traditional dashboards; instead, your mission is twofold: Deep Strategic Analysis: Tackle our most complex product questions (e.g., understanding the true impact of our GenAI features on WAU/DAU retention and user habit formation). AI-Native Data Democratization: Architect the infrastructure that allows our PMs to query our BigQuery database using natural language. You will be the tip of the spear in transitioning our product analytics model from a "request-and-wait" dashboard culture to a real-time, AI-empowered ecosystem. Who You Are: Deep Product & Business Intuition: Demonstrated ability to understand product strategy and key metrics Communication & Influence : Exceptional written and verbal communication skills, with a proven track record of presenting complex data insights clearly and persuasively to both technical and non-technical stakeholders. Proven ability to build strong working relationships and influence decision-making across cross-functional teams, particularly with Product Management. The "Stats-First" Mindset: As AI makes querying easier, knowing which statistical tests to apply and how to interpret the noise is your superpower. Technical Fluency (SQL & AI): Expert-level SQL is a must. You don't need to be a core software engineer, but you must be comfortable using Python and working with APIs, LLMs, and agentic frameworks. Curiosity & Adaptability: The AI tooling landscape changes rapidly. You are the kind of person who actively explores new frameworks, and experiments with how to apply them to business problems. Collaborative Leadership: Ability to work seamlessly with a highly technical remote data team (India) while acting as the strategic face of data for the product leadership team (NYC). Location & Alignment: Ability to effectively collaborate and interact in real-time with Product teams primarily based in NYC, while also being able to work effectively with a team in India. What You’ll Do:   Be the Strategic Co-Pilot for PMs: Co-locate with the Product team to understand the business deeply. Proactively look across all product features to identify trends, drop-offs, and opportunities that go beyond feature-by-feature reporting. Lead the effort to translate complex data analysis into clear, concise, and compelling narratives that drive key business decisions and product feature development decisions.  Deep-Dive Analysis: Conduct and oversee comprehensive, complex analyses of product usage and adoption. Go beyond surface-level metrics to identify root causes for trends. Project Oversight: Act as a thought partner to Product leadership, proactively identifying opportunities and risks through data analysis. Collaborate closely with Product Managers to define key performance indicators (KPIs), establish success metrics for new features, and provide ongoing, proactive insights into product performance. Solve High-Stakes Business Questions: Use advanced statistical methods (e.g., cohort analysis, propensity matching, causal inference, etc.) to answer critical executive and investor questions—such as whether new product capabilities are actually inflecting long-term user retention. Build the AI-to-Data Bridge: Wire up our BigQuery data warehouse to modern LLMs using standard protocols (like Model Context Protocol / MCP) or native clo