Manager 2, Data Science

Category Data Location Mountain View, California Job ID 2024-57445

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Company Overview

Intuit is the global financial technology platform that powers prosperity for the people and communities we serve. With approximately 100 million customers worldwide using products such as TurboTax, Credit Karma, QuickBooks, and Mailchimp, we believe that everyone should have the opportunity to prosper. We never stop working to find new, innovative ways to make that possible.

Job Overview

Intuit is seeking a Manager 2, Data Science to join Intuit AI team.

Come join our collaborative and creative group of data 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 data scientists
Mentor and hire the best data 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, data 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 data 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

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 data science teams that have successfully delivered data-driven software products.
Innovative and disruptive technology leader solving long term strategic outcomes
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.
Impeccable attention to detail and strong ability to convert complex data into insights and action plans
Strategic thinker, flexible problem solver, great listener and team orientation
Able to effectively communicate a vision and inspire others to innovate
You thrive on ambiguity and enjoy the frequent pivoting that’s part of the exploration. You are extremely passionate about applying practically Machine Learning to real customer problems, powering the next generation experiences for large scale applications.

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Intuit provides a competitive compensation package with a strong pay for performance rewards approach. The expected base pay range for this position is Bay Area California $197,500 - 267,000. This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing pay equity for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.