Staff Machine Learning Developer

3 weeks ago

Toronto, Canada Intuit Full time

Company Overview

Intuit is the global financial technology platform that powers prosperity for the people and communities we serve. With approximately 100 million customers worldwide using products such as TurboTax, Credit Karma, QuickBooks, and Mailchimp, we believe that everyone should have the opportunity to prosper. We never stop working to find new, innovative ways to make that possible.

Qualifications

BS, MS, or PhD degree in Computer Science or related field, or equivalent work experience. 6+ years of experience Knowledgeable with Data Science tools and frameworks (. Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark). Basic knowledge of machine learning techniques (. classification, regression, and clustering). Understand machine learning principles (training, validation, Knowledge of data query and data processing tools (. SQL) Computer science fundamentals: data structures, algorithms, performance complexity, and implications of computer architecture on software performance (., I/O and memory tuning). Software engineering fundamentals: version control systems (. Git, Github) and workflows, and ability to write production-ready code. Experience deploying highly scalable software supporting millions or more users Experience with GPU acceleration (. CUDA and cuDNN) Experience with integrating applications and platforms with cloud technologies (. AWS and GCP) Strong oral and written communication skills. Ability to conduct meetings and make professional presentations, and to explain complex concepts and technical material to non-technical users

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.

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