Machine Learning

As a ML Engineer, you will design and deliver innovative Machine Learning solutions as part of intelligent products on Databricks and H2O by using core cloud data science tools, ML Ops components, and other big data related technologies. This includes helping lead and craft projects in their initial phases and deliver them with a team. Additionally, you will collaborate closely with a variety of stakeholders across the Workday organization and the Enterprise Data and Architecture teams.What You will DoImplement models and algorithms on Databricks and H2OWrite in Python or other languages to deliver a wide variety of Machine Learning & Data Science solutionsBuilding tools to accelerate feature experimentation and exploration with lineage, data privacy protection and easier model performance debugging.Stay abreast of new tools, packages, and Machine Learning techniques while consistently pushing the limit of what is possible to deliver the best solutions for clientsManage AWS assets including compute, storage, ID management.Collaborate with Product Owners to apply Workdays agile processes and be responsible for the initiation, delivery, and communication of projects for the stakeholder organizationsBuilding ML-as-a-service, with the purpose of taking experiments to production quickly.Share learnings and project findings with the wider Workday Machine Learning CommunityWilling to work across multiple time zones.What You will Bring5+ years of technology industry experience3 - 4+ years of experience implementing models or machine learning algorithms in productionProficient experience writing SQL, SparkExperience on any of these cloud platforms (AWS, Azure, GCP)Experience developing models and other data science work with Python (preferred) or R(Preferred) Familiarity with ML Ops pipelines or CI/CD implementations(Optional) Quantitative graduate degree(Optional) One or more of the following other languages: Python, R, JavaScript, Go, Scala(Optional) Experience acting as a project manager