The Machine Learning Platform Engineer is a cross-disciplinary role on the Analytics team, leveraging a background in Client research and technical skills to contribute to new analytics product development.
The research aspects of the role include the study and application of state-of-the-art machine learning methods in the context of the Analytics Platform. The candidate will work with leadership and research teams to help guide new and existing predictive analytics efforts; the candidate will provide up-to-date literature reviews and appropriate methodological knowledge for complex predictive analytics problems.
The development aspects of the role involve translating the research into new analytics functionality for the platform. The candidate will support the building of predictive analytics capacity for the platform. This means direct feedback to leadership on appropriate methods, techniques, and feasibility of developing machine learning applications. The willingness to explore and deeply understand the data ODH works with is a must. The candidate will be required to propose methods for solving predictive analytics problems and provide experience-based solutions. The candidate will require the ability to convey complex ideas, learn on the fly, and put newly acquired knowledge into practice. The candidate will work closely with the following groups: Advanced Analytics, Software Engineering, and other clinical and commercial teams.
Ideal candidates will require experience in the following areas: research using statistical modeling, machine learning, and general data analytics methodologies. Some experience with cloud-based analytics technologies and an understanding of healthcare data are strongly recommended. The candidate must have a research-oriented mindset. They must be able to adjust their communication style to diverse types of stakeholders (analyst, business, technical).
• Experience with Python and one or more Client libraries, i.e., TensorFlow, PyTorch, scikit-learn
• Experience in data pre-processing techniques for statistical and machine learning applications
• Experience developing and defending rationale for analytics research
• Experience driving the development of new research technology
• Experience with creating data visualizations to support the communication of ideas
• Excellent communicator
• BA/BS Degree
• Experience with cloud-based, distributed data systems, i.e., Snowflake, Hadoop, Spark
• Experience in the healthcare industry
• Experience with population-level analytics
• Experience developing presentations to convey ideas (storytelling)
• Authorship in machine learning literature or publication
• Academic and/or industry research experience
• Degree in Quantitative Discipline (e.g., physics, engineering, computer science, applied mathematics, etc.)