Welcome! We’re excited you’re considering an opportunity with us! To apply to this position and be considered, click the Apply button located above this message and complete the application in full. Below, you’ll find other important information about this position. Under the general supervision of the AVP Chief Data Officer, this position creates and delivers Machine Learning models to drive operational improvements, automate tasks, augment human performance, provide analytics, risk scoring and surveillance solutions using large datasets and an array of advanced data science technology. Leads all the processes from data collection, data cleaning, data engineering and preprocessing, to training models and deploying them in production. Works with complex data environments in healthcare using information in electronic medical records, healthcare claims, unstructured text data, diagnostic images and other data sources. Collaborates with a multi-disciplinary team of data scientists, software engineers, and subject domain experts to identify and manage high value opportunities. MINIMUM QUALIFICATIONS: EDUCATION, CERTIFICATION, AND/OR LICENSURE: 1. Bachelor's degree in Machine Learning, Computer Science, Computer Linguistics, Mathematics, or related field AND three (3) years of experience with Machine Learning techniques with in-depth understanding of machine learning algorithms and modeling OR Master’s Degree AND one (1) year of experience with Machine Learning techniques with in-depth understanding of machine learning algorithms and modeling PREFERRED QUALIFICATIONS: EDUCATION, CERTIFICATION, AND/OR LICENSURE: 1. Publications at top-tier peer-reviewed conferences or journals. 2. Successful Kaggle competition with top ranking. EXPERIENCE: 1. Experience with healthcare and/or finance. 2. Experience in data science, mathematics. 3. 3 years of software development experience. 4. Experience in programming languages like Python and Java. 5. Experience working with cloud-based services and systems including one or more of Amazon ML, Microsoft Azure, Google Cloud ML, or similar. 6. Experience in object-oriented design, data structures, high-performance computing. 7. Experiences using system monitoring tools and automated testing frameworks. 8. Experience delivering systems and services with large scale deployment of machine learning products. 9. Experience in building natural language processing and computer vision systems. Job Description 01:56 PM 01/09/2024 Page 2 of 3 10. Experience with Agile and Scrum Software Development methodologies. 11. Experience with SOA standards, including SOAP, REST, WSDL, XML, XSD, XSLT, UDDI. CORE DUTIES AND RESPONSIBILITIES: The statements described here are intended to describe the general nature of work being performed by people assigned to this position. They are not intended to be constructed as an all-inclusive list of all responsibilities and duties. Other duties may be assigned. 1. Data mining, data cleaning, data engineering. 2. Select features, build, and optimize classifiers for the use of machine learning techniques. 3. Develop new ML algorithms to find predictive patterns. 4. Establish meaningful criteria for evaluating algorithm performance and suitability. 5. Automate model training and testing and deployment via machine learning continuous delivery pipelines. 6. Implement working, scalable, production-ready Machine Learning and AI Process Automation models and code. 7. Optimize processes for maximum speed, performance and accuracy. 8. Keep up to date with Machine Learning best practices and evolving open source frameworks. 9. Work in an agile team in a scrum process, collaborating closely with software engineers, data scientists, data engineers, subject domain experts and QA analysts. PHYSICAL REQUIREMENTS: The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. WORKING ENVIRONMENT: The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. 1. Normal office environment. SKILLS AND ABILITIES: 1. Knowledge of software engineering practices and best practices for the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations. 2. Applied statistics skills. 3. Good written and verbal communication skills, including technical writing and PowerPoint presentations. 4. Adept at presenting complex topics, influencing and executing with timely and actionable follow-through. 5. Ability to clearly and concisely communicate with technical and non-technical customers both verbally and in writing. 6. Thorough understanding of the principles of data security, particularly personally-identifiable information and protected health information. 7. Thorough understanding of copyright compliance, intellectual property, and corporate identity programs. Additional Job Description: Scheduled Weekly Hours: 40 Shift: Exempt/Non-Exempt: United States of America (Exempt) Company: SYSTEM West Virginia University Health System Cost Center: 576 SYSTEM IT Artificial Intel Analytics

• Design, build, and deploy end-to-end machine learning models using proprietary datasets • Own the full ML lifecycle, including data preparation, feature engineering, model training, evaluation, and validation • Collaborate with Data Scientists to identify and prioritize high-impact ML opportunities • Work with Principal AI Engineers to ensure models align with system architecture and engineering best practices • Package and expose trained models as scalable APIs for consumption by AI Engineers and downstream systems • Build and maintain model pipelines, including versioning, retraining, and continuous improvement workflows • Monitor model performance in production, ensuring accuracy, reliability, and relevance over time • Troubleshoot model drift, data quality issues, and performance bottlenecks.

• Review and refine AI-generated content related to MLOps workflows, machine learning pipelines, automation, monitoring, and deployment. • Evaluate outputs for technical validity, reproducibility, and industry best practices in MLOps. • Draft realistic scenarios covering pipeline orchestration, CI/CD for machine learning, model serving, monitoring, drift detection, and scaling infrastructure. • Assess AI reasoning in topics such as containerization, cloud platform deployment, data versioning, experiment tracking, and model lifecycle management. • Identify gaps or inaccuracies in approaches to operationalizing machine learning. • Create scenario variations from the perspective of different MLOps stakeholders: data scientists, engineers, DevOps, and business leaders.

• Review and refine AI-generated outputs related to machine learning, model development, implementation, and practical aspects of engineering work • Evaluate AI responses for technical accuracy, practicality, and compliance with real-world requirements • Draft realistic ML scenarios based on your direct professional experience • Create scenario variations from different perspectives (e.g. engineer, product manager, client, or stakeholder) • Identify gaps, oversights, or weak reasoning in AI-generated engineering content