Data Scientist, Revenue Analytics

Autodesk

Data Scientist optimizing revenue performance across customer lifecycle for Autodesk. Collaborating with Marketing, Finance, and Product teams for statistical models and analytical frameworks.

Posted 7/7/2026full-timeRemote • 🇨🇦 CanadaMid-LevelSenior💰 CA$101,000 - CA$147,400 per yearWebsite

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

bonuses
stock grants
comprehensive benefits package

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

Statistical InferenceRegression AnalysisHypothesis TestingExperimental DesignData VisualizationForecasting ModelsCustomer Acquisition Cost (CAC)Return on Advertising Spend (ROAS)Customer RetentionChurn Analysis

Soft Skills

Excellent Communication SkillsStorytelling with Data