Machine Learning Engineer

Morgantown, WV 26506

Posted: 08/14/2019 Job Number: 201525 Pay Rate: $130,000.00
POSITION SUMMARY: Under the general supervision of the AVP Chief Data Officer, the Machine Learning Engineer 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.
2. 3 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 at top-tier peer-reviewed conferences or journals.
2. Successful Kaggle competition with top ranking.
3. Experience with healthcare and/or finance.
4. Experience in data science, mathematics.
5. 3 years of software development experience.
6. Experience in programming languages like Python and Java.
7. Experience working with cloud-based services and systems including one or more of Amazon ML, Microsoft Azure,
Google Cloud ML, or similar.
8. Experience in object-oriented design, data structures, high-performance computing.
9. Experiences using system monitoring tools and automated testing frameworks.
10. Experience delivering systems and services with large scale deployment of machine learning products.
11. Experience in building natural language processing and computer vision systems.
12. Experience with Agile and Scrum Software Development methodologies.
13. 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 work being performed by people assigned to this position. They are not intended to be constructed as an all-inclusive list 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.


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.
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