Manager 2, Data Science Job in Intuit

Job Overview Intuit is a global technology platform that helps consumers and small businesses overcome their mostimportant financial challenges. Serving more than 100 million customers worldwide, we believe thateveryone should have the opportunity to prosper. We never stop working to find new, innovative ways tomake that possible. Qualifications BS, MS, or PhD degree in Computer Science or related field, or equivalent practical experience. Knowledgeable with Data Science tools and frameworks (i.e. Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark). Basic knowledge of machine learning techniques (i.e. classification, regression, and clustering). Understand machine learning principles (training, validation, etc.) Knowledge of data query and data processing tools (i.e. SQL) Computer science fundamentals: data structures, algorithms, performance complexity, and implications of computer architecture on software performance (e.g., I/O and memory tuning). Software engineering fundamentals: version control systems (i.e. Git, Github) and workflows, and ability to write production-ready code. Mathematics fundamentals: linear algebra, calculus, probability Interest in reading academic papers and trying to implement state-of-the-art experimental systems Experience using deep learning architectures Experience deploying highly scalable software supporting millions or more users Experience with GPU acceleration (i.e. CUDA and cuDNN) Experience with integrating applications and platforms with cloud technologies (i.e. AWS and GCP) Minimum experience of at least 5 years on relevant technologies Responsibilities Discover data sources, get access to them, import them, clean them up, and make them machine learning ready . Work with data scientists to create and refine features from the underlying data and build pipelines to train and deploy models. Partner with data scientists to understand, implement, refine and design machine learning and other algorithms. Run regular A/B tests, gather data, perform statistical analysis, draw conclusions on the impact of your models. Work cross functionally with product managers, data scientists and product engineers, and communicate results to peers and leaders. Explore new technology shifts in order to determine how they might connect with the customer benefits we wish to deliver.