Senior Data Scientist, Expert Network

Job title: Senior Data Scientist, Expert Network

About the Role

Join the Intuit Customer Success team as a Senior Data Scientist within our Expert Network to shape the AI strategy for product support and live experiences. You’ll lead advanced data science techniques to influence Customer Success strategy and scale AI-driven solutions, partnering across Product Management, Engineering, Data, Customer Success, and Service Delivery.

What You'll Do

• Design, build, and deploy scalable models (ensemble methods, time-series forecasting, LTV modeling, deep learning architectures, and uplift modeling) to uncover high-impact growth opportunities and drive personalization.
• Own the end-to-end experimentation pipeline—from hypothesis generation and design (e.g., CUPED, multi-armed bandits, Bayesian Inference) to rigorous causal interpretation and impact quantification.
• Lead causal inference and econometric analyses to understand and influence key levers of business growth with a crisp understanding of incremental impact.
• Define and evolve success metrics using state-of-the-art measurement frameworks, ensuring KPIs are both predictive and causally informative.
• Deliver compelling, data-driven narratives to VP and Director stakeholders; distill complex findings into clear, actionable strategy recommendations with quantified business impact.
• Demonstrate extreme ownership across cross-functional initiatives, influencing product vision and delivering measurable impact through analytics innovation.

What We're Looking For

• Master’s degree in Computer Science, Statistics, Econometrics, Data Science, or a quantitative field.
• 4+ years of progressive experience in applied data science roles with increasing scope and complexity.
• Proven experience applying state-of-the-art machine learning and causal inference methodologies in high-impact, product-facing applications.
• Expert-level proficiency in SQL as well as Python or R.
• Demonstrated success integrating ML models into production environments, especially within personalization, recommendation, or AI-assisted UX.
• Deep understanding of Generative AI and other evolving technologies to accelerate insights.
• Deep knowledge of experimental design, including non-standard A/B testing methods, uplift modeling, and sequential testing.