About the job
Intuit is seeking a Manager 2, AI Science to join Intuit AI team. Come join our collaborative and creative group of AI scientists and machine learning engineers and build models that directly affect hundreds of thousands of our customers. In this role you will be building and deploying machine learning models using both analytical algorithms and deep learning approaches. We are waiting for you to join us and do the best work of your life.
Responsibilities
Lead and create a team of AI scientists
Mentor and hire the best AI scientists in the valley
Apply artificial intelligence and machine learning techniques to solve complex questions or fuel new business opportunities
Deliver breakthrough benefits to Intuit users/customers across small business & consumer products using individual, enriched, and aggregated data
Provide leadership in advanced engineering, AI science and analytics in the development of current or future products or technologies
Provide technical leadership across multiple teams, by understanding a key technology space deeply enough to help guide strategy
Provide/inspire AI science innovations that fuel the growth of Intuit as a whole
Understand and teach proven methods and hacking skills in working with divergent data types at scale, to explore and extrapolate data-driven insights using advanced, predictive statistical modeling and testing applied to data acquired and cleansed from a range of sources
Provide to business stakeholders the entrepreneurial guidance essential for appropriately interpreting and building on findings, and fully exploiting the insights revealed through the research
Qualifications
Minimum
BS, MS, or PhD in an appropriate technology field (Computer Science, Statistics, Applied Math, Econometrics, Operations Research, Physics, etc.)
2+ plus years experience as an established technical leader/Manager of AI science teams that have successfully delivered data-driven software products.
Expert command of AI and machine learning, statistical modeling, state-of-the-art tools, and engineering best practices
Experience in leading teams who have expertise in data mining algorithms and statistical modeling techniques such as clustering, classification, regression, decision trees, neural nets, support vector machines, genetic algorithms, anomaly detection, recommender systems, and natural language processing.
Preferred
No preferred qualifications listed.