Machine Learning Engineer Principal

Position Summary

Position Summary
:
The Machine Learning Engineer (MLEA) Principal will lead research and development efforts to advance machine learning applications within a hospital setting. This role is responsible for developing innovative algorithms and models to improve patient care, operational efficiency, and clinical outcomes. This role requires extensive expertise in machine learning, cloud deployment, and data engineering, with a strong emphasis on applied research and experimentation.

• Lead and conduct advanced research in machine learning and artificial intelligence to develop novel algorithms and methodologies tailored to healthcare applications.
• Design and implement experiments to test and validate new machine learning models and techniques, focusing on improving patient care and hospital operations.
• Lead methodological research and implementation of methods to adjust for data set shift for healthcare applications
• Collaborate with clinical staff, academic institutions, research labs, and industry partners to stay at the cutting edge of machine learning research and its applications in healthcare.
• Publish research findings in top-tier conferences and journals, and present at industry events and seminars.
• Develop and deploy state-of-the-art machine learning models using iterative development processes, based on statistical approaches and data mining techniques.
• Identify and implement the most optimal modeling techniques based on available data types and objectives/use cases (supervised, unsupervised, semi-supervised, or reinforcement learning).
• Implement highly efficient automated processes that produce modeling results at scale.
• Review current offerings and future developments in artificial intelligence and machine learning and socialize these with key stakeholders to understand needs and potential use cases in the hospital.
• Perform validation of machine learning models for accuracy and develop recommendations for enhancements based on localized data, monitor their performance post-implementation, and fine-tune for optimal results.
• Create clear documentation of workflows, methodologies used, and assumptions built in for various levels of technical expertise.
• Engage in the deployment and integration of predictive models and artificial intelligence into development and production environments within the hospital.
• Advance the department’s capabilities in technical and analytical areas by proactively building partnerships and collaborating with cross-functional teams.
• Contribute to a culture of innovation, collaboration, and continuous improvement by following the latest developments in machine learning research and technology trends.
• Able to expertly maintain existing models as well as deployment new models in both Epic and Non-Epic environments
• Stay up to date with the latest changes from Epic to their analytics and predictive modeling applications through (e.g.) Nova Notes
• Must be able to perform the professional, clinical and or technical competencies of the assigned unit or department.
• These statements are intended to describe the essential functions of the job and are not intended to be an exhaustive list of all responsibilities. Skills and duties may vary dependent upon your department or unit. Other duties may be assigned as required.

• Bachelors Degree in Computer Science, Mathematics, Statistics, Engineering, Economics, or another computational/quantitative field (or equivalent experience)
• 7 or more years of experience using data mining/analytical methods and associated tools such as Python, R, etc.
• 7 or more years of experience with SQL in a relational database or an equivalent combination of education and experience
• 5 or more years of experience with various machine learning methods: unsupervised learning, semi-supervised, supervised learning, as well as anomaly detection, natural language processing and dimensionality reduction
• 5 or more years of experience with containerization and orchestration tools such as Docker and Kubernetes
• 5 or more years of experience with cloud computing platforms such as Azure
• 3 or more…