Data Scientist

Company Name: Intuit
Location: Mountain View, CA
Date Posted: 28th Oct, 2016
  • As a member of a central organization, help shape and drive the strategy for data capture and import that supports Intuit's rich and diverse portfolio of businesses and enables us to continue to accelerate revenue growth over the next several years.
  • Collaborate closely with engineering, product design, and customer support to understand their key challenges and drive improvements into their respective functions.
  • Apply world-class applied machine learning techniques to build models to enable scalable, high-performance distributed document capture and extraction services using computer vision and supplemental data enrichment.
  • Act as an industry thought leader to continue to build up awareness of Intuit as an innovative analytics practitioner in the broader technology community. This will involve speaking at industry events, sharing best-practices with other companies, building working relationships with key analytic experts, firms, and consultants, and more.
  • Act as a core member of the team and help drive and resolve broader team and business issues around our general state of analytics excellence.
  • Outstanding demonstrated business acumen - The candidate must be able to function and lead effectively in multi-disciplinary teams that include business and technical contributors. In particular, the candidate must be comfortable with ambiguity and yet able to steer analytics projects toward clear business goals, testable hypotheses and action-oriented outcomes.
  • Deep mastery of machine learning - Candidate must have a deep mastery of a wide range of ML techniques, tools, and methodologies with a demonstrated capability to apply them to a broad range of business problems and data sources. Machine learning techniques include: clustering, classification, regression, decision trees, neural nets, support vector machines, genetic algorithms, anomaly detection, association rules, sequential pattern discovery, and text mining. This individual will need to demonstrate the highest level of analytic capability, and must have a proven track record in making analysis actionable to improve business outcomes in a product development context.
  • Data proficiency - The candidate must be comfortable working with large data, and be proficient in extracting relevant feature data from relational and non-relational databases. The candidate must also be comfortable with computer vision techniques for feature extraction, classification, etc.
  • Strong algorithmic and programming skills-The candidate must have the skills to design and program new machine learning algorithms as necessary, including, in some cases, large scale implementations on distributed systems.
  • Good working knowledge on Computer Vision and image classification.
  • M.S. degree in an appropriate technology field (computer science, statistics, econometrics, operations research). Ph.D. degree in a quantitative field is a plus.