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!
In this role, you’ll be embedded inside a vibrant team of data scientists. You’ll be expected to help conceive, code, and deploy data science models at scale using the latest industry tools. Important skills include data wrangling, feature engineering, developing models, and testing metrics.
Perform hands-on data analysis and modeling with huge data sets.
Apply data mining, NLP and machine learning (both supervised and unsupervised) to improve our 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, Econometrics, Operations Research).
3+ years experience with data science and/or software engineering.
Expertise in modern advanced analytical tools and programming languages such as R or Python with scikit-learn.
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
Large-scale graph algorithms, clustering, page-rank, and community detection.
Hadoop, HDFS, Hive, HBase, MapReduce, and Mahout.
Apache Spark, SparkSQL, MLlib, and Scala Actors.
Ensemble Methods, Deep Learning, and other trendy topics in the Machine Learning community.