Lead Machine Learning Engineer

Lead Machine Learning Engineer - NIKE USA INC - Beaverton, OR. Develop and program integrated software algorithms to structure, analyze and leverage data in product and systems applications in both structured and unstructured environments; develop and communicate descriptive, diagnostic, predictive and prescriptive insights/algorithms; use machine language and statistical modeling techniques such as decision trees, logistic regression, Bayesian analysis and others; develop and evaluate algorithms to improve product system performance, quality, data management and accuracy; use current programming language, technologies to translate algorithms and technical specifications into code; complete programming and implement efficiencies, perform testing and debugging; complete documentation and procedures for installation and maintenance; apply deep learning technologies to give computers the capability to visualize, learn and respond to complex situations; adapt machine learning to areas such as virtual reality, augmented reality, artificial intelligence, robotics and other products that allow users to have an interactive experience; and work with large scale computing frameworks, data analysis systems and modeling environments. Telecommuting is available from anywhere in the U.S., except from SD, VT, and WV. 

Employer will accept a Master’s degree in Computer Science or Statistics and two (2) years of experience in the job offered or in a computer-related occupation.  
 
Experience must in the following:  

• Developing and delivering production code in languages such as Python, Golang, Java, and Scala 

• Frameworks including Spark and Hadoop 

• AI/ML techniques such as neural networks, tree ensembles, regressions, and hypothesis testing 

• Building Data and ML pipelines using Scikit-learn, Tensorflow, spark ML (MLlib), and OpenCV 

• SQL 

• Architecting and delivering cloud solutions using Google Cloud and AWS 

• Recommendation and search algorithms including ALS, Neural Nets, Clustering, personalization techniques, time series, NLP, and image modeling 

• A/B testing 

• End-to-end lifecycle of ML systems including ML engineering development and designing and building low latency real-time systems 

• Delivering and supporting high impact solutions across large datasets to production; 

• Spark streaming 

• MLOps and the lifecycle of model development from experimentation to production and measurement and visualization