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Machine Learning Engineer Principal

The University of Kansas Health System -
Shawnee Mission, KS

Apply Now

Job Details

Full-time

Qualifications

Containerization systems
Data mining
Text mining tools
Public Cloud
SQL
Machine intelligence
Outlier detection
Supervised learning
Epic Certification
Machine learning (ML) fundamentals
Unsupervised learning
Dimensionality reduction
Healthcare software applications
Database software proficiency

Full Job Description

Position Title

Machine Learning Engineer Principal

Broadmoor Campus

Position Summary / Career Interest:

The Machine Learning Engineer (MLEA) Principal will lead research and development efforts to advance machine learning applications within a hospital setting. This role is also 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.

Responsibilities and Essential Job Functions

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.

Required Education and Experience

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 years of experience with Nebula, Epic’s cloud computing and modeling platform

Preferred Education and Experience

Master's Degree in a related field OR
Doctorate in a related field
Experience working with business intelligence tools such as Power BI, Qlik, SAP Business Objects, Tableau, etc.
Experience with analytical documentation tools such as Jupyter Notebook
Experience in a relevant industry or environment

Required Licensure and Certification Epic certification in 4 data model(s). If not certified, certification is required within 12 months from employment within 1 Year

Time Type:

Full time

Job Requisition ID:

R-52607

Important information for you to know as you apply:

The health system is an equal employment opportunity employer. Qualified applicants are considered for employment without regard to race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), national origin, ancestry, age, disability, veteran status, genetic information, or any other legally-protected status. See also Diversity, Equity & Inclusion .
The health system provides reasonable accommodations to qualified individuals with disabilities. If you need to request reasonable accommodations for your disability as you navigate the recruitment process, please let our recruiters know by requesting an Accommodation Request form using this link [email protected] .
Employment with the health system is contingent upon, among other things, agreeing to the health-system-dispute-resolution-program.pdf and signing the agreement to the DRP.

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