• Data mining, data cleaning, data engineering. • Select features, build, and optimize classifiers for the use of machine learning techniques. • Develop new ML algorithms to find predictive patterns. • Establish meaningful criteria for evaluating algorithm performance and suitability. • Automate model training and testing and deployment via machine learning continuous delivery pipelines. • Implement working, scalable, production-ready Machine Learning and AI Process Automation models and code. • Optimize processes for maximum speed, performance and accuracy. • Keep up to date with Machine Learning best practices and evolving open source frameworks. • Work in an agile team in a scrum process, collaborating closely with software engineers, data scientists, data engineers, subject domain experts and QA analysts.