Senior Data Scientist

Position Overview 

We are seeking a Senior Data Scientist to lead experimentation and causal inference initiatives for Autodesk's Go-to-Market Data Intelligence (GDI) organization. This role will focus on designing and deploying rigorous measurement frameworks that move beyond descriptive analytics toward defensible, decision-grade impact estimation. 

You will partner with GTM teams across Sales, Customer Success, Marketing, and Finance to quantify incremental impact, support decision-making to improve resource allocation, and elevate experimentation standards across GTM funnels. The ideal candidate combines strong statistical foundations with production-level fluency in Python and SQL and has a demonstrated ability to translate causal insights into operational decisions. 

Responsibilities 

• Design, implement, and evaluate randomized controlled trials (A/B, geo experiments, incrementality tests). 

• Develop quasi-experimental frameworks (e.g., difference-in-differences, synthetic controls, regression discontinuity, instrumental variables, uplift modeling). 

• Define and operationalize an experimentation roadmap across GTM, including hypothesis prioritization, pre-registration standards, guardrail metrics, and clear decision thresholds for launch, scale, or sunset. 

• Establish best practices for experimental design, power analysis, and bias mitigation. 

• Design, build and productionalize causal and predictive models using Python. 

• Develop reusable experimentation and inference toolkits. 

• Conduct robustness checks, sensitivity analyses, and assumption validation. Also partner with analysts on those activities to accelerate project delivery. 

• Partner with data engineering to ensure high-quality, analysis-ready datasets and successful model deployment. 

• Transform and organize complex structured and semi-structured data sources using advanced SQL.  

• Construct scalable analytical datasets from large-scale transactional and behavioral systems. 

• Collaborate on experimentation infrastructure, including randomization frameworks and measurement pipelines. 

• Improve data instrumentation, logging, and tracking to enable defensible inference. 

• Translate statistical findings into clear, decision-oriented recommendations. 

• Influence GTM strategy through evidence-based resource allocation guidance. 

• Educate cross-functional partners on experimental design, causal reasoning, and interpretation of causal inference analytics. 

Key Qualifications 

• 5+ years of experience and a graduate degree in data science, computer science, applied econometrics, statistics, or related quantitative field. 

• Strong grounding in causal inference theory and applied methods. 

• Advanced proficiency in Python (e.g., pandas, NumPy, statsmodels, scikit-learn; experience with causal libraries such as DoWhy, EconML, or similar is preferred). 

• Advanced SQL skills with experience working on large-scale data warehouses. 

• Experience designing and analyzing online or field experiments. 

• Ability to communicate, justify and visualize complex statistical concepts for non-technical stakeholders. 

• Experience in B2B GTM environments (SFDC and MarTech data, sales performance, pricing, marketing mix, lifecycle optimization, or growth experimentation). 

• Familiarity with uplift modeling and heterogeneous treatment-effect estimation. 

• Exposure to Bayesian methods or hierarchical modeling. 

• Experience deploying models in production environments. 

• Experience influencing executive decision-making through formal experimentation readouts or investment cases.