Intuit is looking to drive AI strategy for product support and live experiences, and optimize their greatest resource, their people, through innovating, experimenting, learning, and scaling AI-driven solutions.
Requirements
- Expert-level proficiency in SQL as well as Python or R
- Proven experience applying state-of-the-art machine learning and causal inference methodologies in high-impact, product-facing applications.
- 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 frameworks.
- Hands-on experience with data visualization tools like Tableau or Qlik.
- Foundation in statistical methods, machine learning, experimentation design, and causal inference
- Experience with ensemble methods, time-series forecasting, LTV modeling, deep learning architectures, and uplift modeling
Responsibilities
- Design, build, and deploy scalable models—including 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 that business 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.
- Integrate ML models into production environments, especially within personalization, recommendation, or AI-assisted UX.
Other
- Master’s degree in Computer Science, Statistics, Econometrics, Data Science, or a quantitative field.
- 3+ years of progressive experience in applied data science roles with increasing scope and complexity.
- Strong communication and storytelling abilities—adept at translating sophisticated analytics into strategic guidance.
- Ability to lead with influence, navigate ambiguity, and execute with precision.
- Ability to deliver compelling, data-driven narratives to VP and Director stakeholders
