Data Scientist, Product Analytics

Understand product usage. Work closely with product managers (PMs), building a deep understanding of the product itself, to analyze how customers adopt and use it, and identify patterns in engagement, feature usage, and retention.to Analyze how customers adopt and use the product, and identify patterns in engagement, feature usage, and retention.

Connect usage to outcomes. Link product usage data to customer and sales data to reveal how usage relates to expansion, churn risk, and account health.

Define usage-based signals. Build the logic behind usage scoring, health indicators, and other metrics that translate behavior into insights that drive activation, retention, and expansion. 

Be proactive. Explore product, sales, and customer data to surface findings nobody asked for. Anticipate the next question.

Run experiments and validate. Test which usage signals actually predict outcomes, applying sound statistical methods and being honest about significance and causality.

Partner on the data foundation. Work with Data and Analytics Engineers to get product and sales data into the warehouse and modeled well. Define requirements and contribute to data models that connect product usage with customer and sales data.

Tell the story. Communicate insights clearly to stakeholders across the business, and document context, caveats, and decisions so the work survives handoffs.

Deep product understanding and a track record of working effectively with PMs, translating product knowledge and usage data into decisions they act on. 

Strong analytical track record: someone who has measurably influenced revenue, product, or GTM decisions through analysis, not just produced reports.

Comfort with the latest AI tools, and a habit of using them to work faster and sharper: You stay current as the tooling evolves and bring new approaches to the team.

Eagerness to develop: you actively grow your skills, seek feedback, and treat new tools and methods as opportunities rather than threats.

Solid SQL. You can independently query, join, and explore data without waiting for someone to prepare it for you.

Proficiency in Python for analysis, modeling, and automation. Experience with ML and statistical libraries (e.g. scikit-learn, statsmodels) for modeling, prediction, and inference.

Statistical foundation: experimentation, significance testing, regression, segmentation, forecasting, and the judgment to know which applies.

Working knowledge of how product usage connects to revenue: funnels, cohorts, retention, account health, and the realities of joining product usage data with CRM and sales data.

Willingness to get hands-on with data modeling. You don't need to be a dbt expert, but you must be comfortable exploring messy data and partnering on (or building) the models you need rather than waiting for clean tables.

Strong communication and stakeholder skills: you can challenge weak measurement respectfully and make a recommendation, not just present options.

Proactivity and autonomy: you raise your hand early, plan your own work, and look for impact without being asked.