Data Scientist, Revenue Analytics

🕒 July 7

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Autodesk

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1982

📱 Media

Architecture • Engineering • Media

Autodesk is a global leader in software for designers, engineers, builders, and creators. The company provides a comprehensive suite of design and engineering applications including popular products like AutoCAD, Revit, and 3ds Max. Through its Design and Make Platform, Autodesk empowers professionals across various industries to design, visualize, and manage projects efficiently, facilitating innovation and sustainability in architecture, engineering, construction, and manufacturing.

📋 Description

• Develop statistical models and analytical frameworks that measure the impact of marketing, sales, and customer success initiatives on pipeline, bookings, ARR, renewal, and expansion • Analyze the effectiveness of paid media investments, including channel performance, return on advertising spend (ROAS), customer acquisition cost (CAC), and marketing ROI • Build predictive models to identify drivers of customer acquisition, free trial conversion, customer retention, expansion, and churn • Design and evaluate experiments to measure incremental business impact and inform strategic investment decisions • Partner with cross-functional stakeholders to define success metrics and build measurement strategies across the customer lifecycle • Develop forecasting models to support revenue planning and investment decisions • Communicate analytical findings to senior leaders through clear storytelling and data visualization, translating complex analyses into business recommendations • Continuously improve data quality, measurement methodologies, and analytical best practices across the organization

🎯 Requirements

• Bachelor’s degree in Statistics, Mathematics, Economics, Computer Science, Data Science, or a related quantitative field (Master’s or PhD preferred) • 5+ years of experience applying statistical analysis to solve complex business problems • Strong foundation in statistical inference, regression analysis, hypothesis testing, experimental design, and predictive modeling • Advanced SQL skills with experience building large-scale analytical datasets • Proficiency in Python or R for statistical analysis and machine learning • Experience developing predictive models using techniques such as logistic regression, gradient boosting, random forests, or survival analysis • Strong understanding of SaaS business metrics including ARR, ACV, CAC, LTV, conversion rates, retention, renewal, and expansion • Excellent communication skills with the ability to explain complex analytical concepts to technical and non-technical audiences • Demonstrated ability to influence business strategy through data-driven insights.

🏖️ Benefits

• bonuses • stock grants • comprehensive benefits package