Nike Lead Machine Learning Engineer 

United States, Oregon, Beaverton 

214013493

Lead Machine Learning Engineer -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 aMaster’s degree in Computer Scienceor 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

• production;

• Spark streaming

• and the lifecycle of model development from experimentation toproduction and measurementand visualization