Senior Data Scientist

Position Overview

We are seeking a Senior Data Scientist to leadexperimentation andcausal inferenceinitiatives 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 toimprove resource allocation, and elevate experimentation standardsacrossGTMfunnels. 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 andproductionalizecausal and predictive models using Python.

• Develop reusable experimentation andinferencetoolkits.

• 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 datasetsand successful model deployment.

• Transform and organizecomplex structured and semi-structureddata 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 experienceand a graduate degreein data science,computer science,applied econometrics, statistics, or related quantitativefield.

• Strong grounding in causal inference theory and applied methods.

• AdvancedproficiencyinPython(e.g., pandas, NumPy,statsmodels, scikit-learn; experience with causal libraries such asDoWhy,EconML, or similar is preferred).

• Advanced SQL skills with experience workingonlarge-scale data warehouses.

• Experience designing and analyzing online or field experiments.

• Ability to communicate,justifyand visualizecomplex statistical conceptsfornon-technical stakeholders.

• Experience in B2B GTM environments (SFDC andMarTechdata,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 formalexperimentationreadouts or investment cases.