Data Scientist

A track record of partnering with RevOps and Finance teams to translate business challenges into data-driven solutions, ensuring alignment on key performance metrics.
Proficiency with tools across the modern data stack (python, SQL, BI tools, dbt)

Ensure Data Quality & Integrity: Implement automated data validation and cleansing processes to maintain high-quality datasets, reducing errors that could impact financial reporting or revenue forecasting.
Develop Data Models: Create and maintain data models that drive key performance indicators (KPIs) for revenue operations and finance.
Build Intuitive Dashboards for Revenue Insights: Design and maintain user-friendly dashboards tailored for revenue leaders, focused on key business metrics such as lead conversion rates, pipeline coverage, sales velocity, and win/loss trends. Prioritize clarity and usability, enabling teams to self-serve insights and track performance against targets.
Support Ad Hoc Analysis and Strategic Questions: Act as a thought partner to GTM stakeholders by rapidly answering ad hoc data questions, conducting deep-dive analyses, and providing clear, actionable takeaways grounded in data.
Document Processes & Methodologies: Maintain thorough documentation of data pipelines, models, and analytical methodologies to facilitate transparency and ensure consistency across teams.