Intuit: Staff Data Scientist [Mountain View, CA]

Intuit is seeking a Staff Data Scientist in Mountain View, CA, to perform hands-on data analysis and modeling with huge data sets, and apply data mining, NLP, and machine learning (both supervised and unsupervised) to improve relevance and personalization algorithms.

At: IntuitIntuit
Location: Mountain View, CA
Position: Staff Data Scientist

Apply here.


Intuit is looking for innovative and hands-on machine learning engineers to help the central data science team develop, design and integrate mathematical models into production. Our team builds AI/ML solutions for all the internal teams within Intuit like Engineering, HR, Finance & Legal. We are looking for team members that love new challenges, cracking tough problems and working cross-functionally.  If you are looking to join a fast-paced, innovative and incredibly fun team, then we encourage you to apply.  Come do the best work of your life!


  • Responsibilities:
    • Perform hands-on data analysis and modeling with huge data sets.
    • Apply data mining, NLP, and machine learning (both supervised and unsupervised) to improve relevance and personalization algorithms.
    • Work side-by-side with product managers, software engineers, and designers in designing experiments and minimum viable products.
    • Discover data sources, get access to them, import them, clean them up, and make them “model-ready”. You need to be willing and able to do your own ETL.
    • Create and refine features from the underlying data. You’ll enjoy developing just enough subject matter expertise to have an intuition about what features might make your model perform better, and then you’ll lather, rinse and repeat.
    • Run regular A/B tests, gather data, perform statistical analysis, draw conclusions on the impact of your optimizations and communicate results to peers and leaders.
    • BS, MS, or PhD in an appropriate technology field (Computer Science, Statistics, Applied Math, Operations Research)


    • Technical pre requisites
    • 3+ year’s experience with data science
    • Expertise in modern advanced analytical tools and programming languages such as R or Python with scikit-learn.
    • Fluent in SQL, Hive, SparkSQL, etc.
    • 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, sequential pattern discovery, and text mining
    • Solid communication skills: Demonstrated ability to explain complex technical issues to both technical and non-technical audiences

    Preferred Additional Experience:

    • Apache Spark and the Hadoop ecosystem.
    • HP Vertica
    • Ensemble Methods, Deep Learning, and other topics in the Machine Learning community.

    Imagine a career where your creative inspiration can fuel BIG innovation. Year-over-year, Intuit has been recognized as a best employer and is consistently ranked on Fortune's "100 Best Companies To Work For" and Fortune World's "Most Admired Software Companies" lists. Immerse yourself in our award winning culture while creating breakthrough solutions that simplify the lives of consumers and small businesses and their customers worldwide.

    Intuit is expanding its social, mobile, and global footprint with a full suite of products and services that are revolutionizing the industry. Utilizing design for delight and lean startup methodologies, our entrepreneurial employees have brought more than 250 innovations to market – from QuickBooks® and TurboTax®, to GoPayment,, big data, cloud (SaaS, PaaS) and mobile apps. The breadth and depth of these customer-driven innovations mean limitless opportunities for you to turn your ingenious ideas into reality at Intuit.

    Discover what it's like to be part of a team that rewards taking risks and trying new things. It's time to love what you do! Check out all of our career opportunities at: EOE AA M/F/Vet/Disability

    Intuit will consider for employment qualified applicants with criminal histories in a manner consistent with requirements of local law.