Intuit is looking for an innovative and hands-on Senior Data Scientist to join the Intuit AI team.
This team embeds artificial intelligence and machine learning into our product portfolio and business to create smarter products, improve anti-fraud and security and enhance customer care. 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.
ResponsibilitiesPerform hands-on data analysis and modeling with huge data setsApply data mining, NLP, and machine learning (both supervised and unsupervised) to improve relevance and personalization algorithmsWork side-by-side with product managers, software engineers, and designers in designing experiments and minimum viable productsDiscover 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 ETLCreate 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 repeatRun regular A/B tests, gather data, perform statistical analysis, draw conclusions on the impact of your optimizations and communicate results to peers and leadersExplore new design or technology shifts in order to determine how they might connect with the customer benefits we wish to deliverBS, MS, or PhD in an appropriate technology field (Computer Science, Statistics, Applied Math, Operations Research, etc.)1+ years of industry experience with data science1+ years of experience in modern advanced analytical tools and programming languages such as R or Python with scikit-learnEfficient in SQL, Hive, or SparkSQL, etc.Comfortable in Linux environment1+ years of experience in data mining algorithms and statistical modeling techniques such as clustering, classification, regression, decision trees, neural nets, support vector machines, anomaly detection, recommender systems, sequential pattern discovery, and text miningSolid communication skills: Demonstrated ability to explain complex technical issues to both technical and non-technical audiences
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