EBSCO Information Services (EIS) provides a complete and optimized research solution comprised of e-journals, e-books, and research databases — all combined with the most powerful discovery service to support the information needs and maximize the research experience of our end-users. Headquartered in Ipswich, MA, EIS employs more than 3,300 people worldwide. We are the leader in our field due to our cutting-edge technology, forward-thinking philosophy, and top-notch workforce. EIS, a division of EBSCO Industries Inc., based in Birmingham, AL, is ranked in the top 200 of the nation’s largest, privately held corporations according to Forbes magazine. EBSCO is a company that will motivate you, inspire you, and allow you to grow. We are looking for the best. If you are too, we encourage you to explore our unique opportunities.
Note that while working at an EBSCO office is preferred (Durham NC or Ipswich MA), this role can be performed almost 100% remotely. There will be some required travel to Ipswich of 2-3 days per quarter for Agile planning sessions and the occasional trainings or development workshops.
Machine Learning Lead:
The mission of the Machine Learning Lead is to collaborate with the taxonomy and indexing teams to establish, configure, analyze, and adjust automated content classification processes using auto-classification machine learning software and the EBSCO taxonomy and ontology.
The Machine Learning Lead will be responsible for establishing, configuring, and reviewing the setup and output of the machine learning classification using industry metrics, adjusting the iterative process to achieve satisfactory results, and exploring new machine learning capabilities. Additionally, the Machine Learning Lead will be responsible for identifying the necessary steps and timelines required to implement machine learning workflows.
• Lead the semantic enrichment team’s machine learning initiatives by connecting efforts to EBSCO priority features and epics.
• Lead in SAFe practices and lean principles.
• Work with the taxonomy and indexing teams to determine which taxonomy terms are appropriate candidates for machine learning classification.
• Work with the taxonomy team, the indexing team, and subject matter experts to identify candidate documents for machine learning training sets.
• Preprocess documents to ensure compatibility and standardization of format and metadata for documents used with machine learning software.
• Provide suggestions for taxonomy/ontology development or improvement based on machine learning results.
• Establish and configure machine learning workflows and automatic categorization models for EBSCO content, value streams, and markets.
• Evaluate machine learning output using industry standard metrics, including precision, recall, accuracy, and F-measure.
• Connect industry standard metrics to customer use data to ensure indexing connects with the user.
• Retrain machine learning software as the EBSCO taxonomy is expanded or revised.
• Work with machine learning vendor and customer support to resolve issues.
• Incorporate multilingual taxonomy and ontology elements into machine learning software as needed.
• Master’s degree in computer science, information science, library science, or a related discipline.
• 2+ years’ experience using machine learning for automated and semi-automated text classification.
• 2+ years’ working with a taxonomy, ontology, or controlled vocabulary.
• 2+ years’ basic programming experience in Python or other language.
• Understanding of machine learning principles, metrics, and best practices such as f-measure, precision, and recall.
• Knowledge of taxonomy/ontology principles, formats, and best practices.
• Familiarity working with multiple standard document formats, including XML, HTML, and PDF.
• Attended an iSchool.
• Experience in computational linguistics, NLP, or linguistics.
• Knowledge of publishing world and processes.
• Knowledge of library and metadata world and processes.
• Experience working with Cogito software (formerly known as TEMIS Luxid) and Expert Systems.
• Experience working with Amazon Web Services (AWS), including SageMaker.
Team Player: Works well with others and enjoys collaborating with immediate team members and across departments. Always willing to lend a hand; moves proactively not reactively.
Technical Prowess: Able to quickly understand existing machine learning workflows and capable of developing technical know-how to improve workflows and team capability over time.
Communication & Presentation: Communicates and presents ideas clearly and with confidence. Delivers presentations and status updates suited to the characteristics and needs of the audience.
Continuous Improvement: Continually focused on improving technical skills and understanding of how machine learning delivers business value.
Enthusiasm: Exhibits high energy, passion and intensity in completing work objectives. Can-do problem-solver.
What you bring to our culture:
Drive - Positive Attitude - Good Judgement - Open Communication – Collaboration – A Desire to Make an Impact - Eager to Understand – Accountability – Decisiveness – You’re a Team Player
If you are interested in hearing more about this opportunity, please send your resume to firstname.lastname@example.org. Thanks for your interest!
EBSCO Industries, Inc.is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws. EBSCO strictly prohibits and does not tolerate discrimination against employees, applicants, or any other covered persons because of race, color, sex (including pregnancy), age, national origin or ancestry, ethnicity, religion, creed, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class. This policy applies to all terms and conditions of employment, including, but not limited to, hiring, training, promotion, discipline, compensation, benefits, and termination of employment.
EBSCO complies with the Americans with Disabilities Act (ADA), as amended by the ADA Amendments Act, and all applicable state or local law.