Principal Scientist

Cambium Learning® Group is an award-winning educational technology solutions leader dedicated to helping all students reach their potential through individualized and differentiated instruction. Using a research-based, personalized approach, Cambium Learning Group delivers SaaS resources and instructional products that engage students and support teachers in fun, positive, safe and scalable environments. These solutions are provided through Learning A-Z® (online differentiated instruction for elementary school reading, writing and science), ExploreLearning® (online interactive math and science simulations, a math fact fluency solution, and a K–2 science solution), Voyager Sopris Learning® (blended solutions that accelerate struggling learners to achieve in literacy and math and professional development for teachers), and VKidz Learning (online comprehensive homeschool education and programs for literacy and science). We believe that every student has unlimited potential, that teachers matter, and that data, instruction, and practice are the keys to success in the classroom and beyond.

Job Overview:

As a Principal Scientist at CAI, you will play an integral part of the machine learning team, which consists of data scientists, psychometricians, linguists, and software engineers. Our team focuses on natural language applications in educational measurement, with key foci in the automated scoring of student responses for both summative and interim assessments, automated feedback for formative assessments, and automated alerting of disturbing content in student responses. Our team uses state-of-the-art deep learning tools and models different modalities (e.g., speech, text) and is responsible for both prototyping and deploying engines and models.

This role is the role of an individual contributor, providing the team guidance on the latest advancements in artificial intelligence, with particular emphasis on deep neural networks and transformer-based architectures. This position will work closely with the team leads in the machine learning team on developing new applications for automated scoring methods and building a scalable architecture.

Job Responsibilities:

• Support CAI’s innovation in natural language applications in support of our mission to improve educational outcomes for our students and educators. This includes automated scoring in large scale assessment programs (for writing, speech and other areas of educational measurement), detection of crisis alert content in student writing and the use of ML for feedback in student writing.
• Support CAI’s emerging product portfolio for products that employ state of the art machine learning methods that provide solutions to real world problems in K-12, higher education and other related domains.
• Contributes to R&D agenda and sets priorities for the automated scoring team
• Drive and provide mentorship to the data scientists in our team
• Design and implement studies to obtain, score, model, and examine results for any of CAI’s current natural language applications, including automated scoring, crisis alert detection, and feedback.
• Pursue applied research to actively publish and represent CAI’s leadership in this area
• Present at conferences and technical advisory committee meetings

Job Requirements:

• Ph.D. in applied mathematics, statistics or related computational field
• Demonstrated experience working with deep neural networks and transformer-based architectures
• Professional experience in large scale assessment or EdTech
• 5+ years working with teams of software developers, NLP scientists, and data scientist
• Demonstrated history of published research in artificial intelligence applications within large scale assessment

To apply for this opportunity, simply click on the “Apply” button and submit a cover letter and resume.

An Equal Opportunity Employer

We are dedicated to fostering a culture that celebrates unique backgrounds, ideas, and experiences. All qualified applicants will receive consideration for employment without discrimination on the basis of race, color, religion, sex, gender, gender identity/expression, sexual orientation, national origin, protected veteran status, or disability.