Intuit Customer Success team is looking to shape the AI strategy for product support and live experiences by optimizing their people resources through AI-driven solutions.
Requirements
- 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. Application of GenAI at scale in a production environment
- 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.
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.
- Collaborate with the Central AI team to productionalize models that enhance personalization and automation throughout the user experience.
- Mentor senior data scientists and establish best practices in experimental design, model validation, and responsible AI usage; drive a culture of analytical excellence and scientific rigor.
Other
- Master’s or PhD degree in Computer Science, Statistics, Econometrics, Data Science, or a quantitative field.
- 10+ 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.
- Proven leadership in mentoring technical talent and driving cross-team alignment through data science innovation.
- Demonstrate extreme ownership across cross-functional initiatives, influencing product vision and delivering measurable impact through analytics innovation.
