Data Scientist, Product

About Hebbia The AI platform for investors and bankers that generates alpha and drives upside. Founded in 2020 by George Sivulka and backed by Peter Thiel and Andreessen Horowitz, Hebbia powers investment decisions for BlackRock , KKR , Carlyle , Centerview , and 40% of the world’s largest asset managers. Our flagship product, Matrix, delivers industry-leading accuracy, speed, and transparency in AI-driven analysis. It is trusted to help manage over $30 trillion in assets globally. We deliver the intelligence that gives finance professionals a definitive edge. Our AI uncovers signals no human could see, surfaces hidden opportunities, and accelerates decisions with unmatched speed and conviction. We do not just streamline workflows. We transform how capital is deployed, how risk is managed, and how value is created across markets. Hebbia is not a tool. Hebbia is the competitive advantage that drives performance, alpha, and market leadership. The Role This is the founding hire for product analytics at Hebbia. Today, we do not have a canonical product metrics layer. We do not have clean fact tables around product usage. The person who steps into this role will define what our core product metrics are: what counts as an active user, what engagement actually means, what signals correlate with retention. This is not a dashboarding role. The goal is to shape product decisions with data, not just report on them. You will identify which workflows drive repeat usage, where users drop off, what features move engagement, and what differentiates power users from casual users across our enterprise customer base. You will turn product intuition into data-backed insight. The role sits at the intersection of analytics engineering, product analytics, and data science. You will build the infrastructure and do the analysis. Define the metrics, build the pipelines, create the dashboards, and use what you built to inform the roadmap. Responsibilities Define and implement Hebbia’s core product metrics from scratch: active users, engagement, retention, feature adoption, account health. Build the canonical definitions the entire company uses. Design and build the product analytics infrastructure: fact tables, clean data models, and the analytics layer that sits on top of our product data. Build and maintain executive and product dashboards that leadership and product teams use to make decisions. Write DAGs, transforms, and data pipelines that support analytics. Work with engineering to instrument the product so usage data is captured correctly. Analyze customer behavior across our B2B customer base: account-level usage patterns, workflow adoption, expansion signals, and churn risk indicators. Inform the product roadmap using data. Identify friction in user flows, surface feature adoption patterns, and highlight opportunities for product improvement. Partner with product managers and engineers to translate product questions into measurable data and structured experiments. Establish data quality standards and documentation so the metrics layer you build is trusted and maintained. Who You Are 3+ years of experience in product analytics, analytics engineering, or data science at a B2B SaaS company or high-growth startup You have defined, implemented, and operationalized product metrics from scratch, ideally at a company where the analytics function did not exist yet Strong in SQL and Python. You can write production-quality transforms, not just ad hoc queries. Experience with modern data stack tools: dbt, Airflow, Snowflake, BigQuery, or similar. You understand data modeling and warehouse architecture. You have built dashboards and reporting that product teams and leadership actually use to make decisions You understand B2B product analytics: account-level metrics, multi-user workflows, enterprise engagement patterns, and why B2B retention analysis is different from consumer You translate ambiguous product questions into struc ... (truncated, view full listing at source)