Machine Learning Engineer

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, the Senior Machine Learning Engineer provides system and engineering expertise in the development, application and integration of AI, machine learning tools, robots, automation and software system. Provides oversight and coordination of strategic AI initiatives across the health system, develops and cultivates relationships across project teams and operational partners, facilitates the development of AI solutions. Evaluates and recommends tools and processes to enhance effectiveness. Coordinates efforts to design, innovate, and implement solutions as needed to improve systems and processes. Creates and delivers AI algorithms to drive operational improvements, automate tasks, augment human performance, provide predictive 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, EXPERIENCE, CERTIFICATION, AND/OR LICENSURE: 1. Bachelor's degree in Machine Learning, Computer Science, Computer Linguistics, Mathematics, or related field AND five (5) years of experience with Machine Learning techniques with in-depth understanding of machine learning algorithms and modeling OR Master’s Degree or a PhD AND one (2) years 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 and presentations 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. 10. Experience with Agile and Scrum Software Development methodologies. 11. Experience with SOA standards, including SOAP, REST, WSDL, XML, XSD, XSLT, UDDI. 12. Deep understanding of Data Science, Machine Learning, Automation, their application, and the effective use of a AI lifecycle; including an ability to articulate the role of MLOps in model development from experimentation to production and measurement. 13. Familiarity with ETL, ML, RPA, analytics technologies such as Scikit-learn, Tensorflow, and other similar platforms and frameworks.

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. 10. Acts as an expert in the area of automation, machine learning and statistics. 11. As senior member of an AI team, facilitate assignments to personnel based on staff skills and knowledge and coordinate the work of other ML engineers, Automation Engineers, Data Scientists and across functional teams, to ensure projects are completed successfully and in a timely manner to deliver innovative and high-quality solutions. 12. Stay ahead of industry trends and recommend relevant technologies & products in the areas of Analytics, Machine Learning, Artificial Intelligence, Automation and Data Science tools and other emerging technologies. 13. Frame real world healthcare operational challenges into AI-driven features and solution offerings Engage operational stakeholders to understand and document workflows, business requirements and identify opportunities for improvement with the application of solutions that leverage Data Science, Automation and ML modeling.

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 communication skills, including technical writing, presentations, and the ability to meaningfully communicate, written, orally, and visually, technical topics with peers and articulate the benefits and tradeoffs of various solutions 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: Day (United States of America) Exempt/Non-Exempt: United States of America (Exempt) Company: WVUH West Virginia University Hospitals Cost Center: 576 SYSTEMIT Artificial Intel Analytics Address: Monroe County Union West Virginia

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