Senior Data Analyst driving Next Best Action strategies using advanced analytics for banking products. Collaborating to enhance customer engagement, onboarding, and financial performance in a fintech environment.
About the role
- Data Scientist optimizing revenue performance across customer lifecycle for Autodesk. Collaborating with Marketing, Finance, and Product teams for statistical models and analytical frameworks.
Responsibilities
- 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
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