About this role
About the Role Join the AI Foundation at Intuit as a Principal AI Scientist. The PDX AI Foundation team focuses on building capabilities that enable and accelerate AI development across Intuit. You will lead efforts to leverage cutting‑edge AI technologies at scale, developing AI standards, evaluation methodologies and tools, while collaborating across engineering, product, analytics and design to accelerate the work of developers. What You'll Do
- Practice leadership and communication skills to influence teams and evangelize data science across the organization
- Collaborate with stakeholders to define success criteria and align model metrics with business goals
- Work side-by-side with product managers, software engineers, and designers in designing experiments and minimum viable products
- Lead technical work of a scrum team: initiating and designing model solutions, driving end-to-end architecture designs of the team’s work, and holding the team accountable for high quality code, git, design, costs and implementation standards
- Perform hands-on data analysis and modeling with large data sets, including discovering data sources, getting data access, cleaning up data, and making them “model-ready”. You need to be willing and able to do your own ETL and design/build featurization
- Apply data mining, NLP, and machine learning (such as supervised/unsupervised, Causal-ML, Online Learning, Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and datasets
- Communicate with partners to ensure successful delivery and integration of DS solutions
- Proactively research, explore, and enable new ML technologies. Keeps up with the new developments in academia and industry and considers possible extensions to solve Intuit customer problems What We're Looking For
- 10+ years of industry experience with data science
- BS, MS or PhD in Statistics, Mathematics, Computer Science, Economics, Operations Research, or equivalent experience
- 8+ years of hands-on expertise in ML paradigms such as Causal-ML, supervised/unsupervised, Online, Bayesian, Reinforcement or Deep Learning
- Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization
- Proficient in NLP techniques, Explainable AI, and ML frameworks
- Experience with modern AI advances and related tooling