Machine Learning Engineer
Morgantown, WV 26506
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
7. Thorough understanding of copyright compliance, intellectual property, and corporate identity programs.