Advanced Specialist, AI Scientist
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AI Summary
About Pearson’s Automated Scoring Team
As the world's learning company, Pearson helps people make more of their lives through learning. We use our knowledge, passion, and reach to tackle the big problems in education and inspire a love of learning that lasts a lifetime. That is why we need smart people like you. Together, we can transform education and provide boundless opportunities for billions of learners worldwide.
The Automated Scoring team develops machine learning-based models that analyze tens of millions of learner exam responses each year. Our technology is unique and meaningful, providing results quickly on student performance on standardized tests. The AI Scientist will join Pearson’s Automated Scoring Team to provide support for the administration of Pearson’s automated scoring programs and support the execution of initiatives to innovate and improve the delivery of Pearson's automated scoring technologies. This role will report to and work closely with the Manager of AI Scientist, but it will also support program managers, quality assurance automation engineers, psychometricians, and various internal stakeholders to ensure the quality and reliability of our automated scoring systems.
AI Scientist’s Duties & Responsibilities
Listed below are the typical duties and responsibilities expected of an individual for the job title. The items specified below are a guideline of the minimum expectations for the job title; changes will be made on a case-by-case basis for individuals who show potential to take on more opportunities.
Train, evaluate, and deploy machine learning models tasked with scoring short answer and essay student responses to formative and summative test administrations from school districts nationwide
Monitor performance of deployed machine learning models to ensure consistent, fair, and unbiased scoring in real time and recalibrate deployed models as needed
Maintain, update, and improve code base used to train and deploy machine learning models
Evaluate historical model performance and conduct experiments exploring strategies to potentially improve team modeling techniques and approaches
Research and stay up-to-date on emerging technologies in the NLP space
Be a thought leader in automated scoring by attending and presenting at conferences
Qualifications
Qualified individuals will be required to work with dynamic teams driven by project delivery goals. They should possess the drive to learn and continuously improve on work performance. They must also be detail-oriented and eager to work with peers in producing quality output. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Bachelor’s degree in a quantitative field (CS, EE, statistics, math, data science)
2+ years professional experience as a software engineer, data scientist, psychometrician, machine learning engineer
Solid understanding of machine learning principles and current/emerging technologies
Strong coding & analytics skills including proficiency in Python and Linux commands
Understanding of or experience with deploying machine learning models into production environments
Familiarity with software engineering fundamentals (version control, object-oriented and functional programming, database and API access patterns, testing)
Passionate about agile software processes, data-driven development, reliability, and systematic experimentation
Strong verbal and written communication skills including the ability to interact effectively with colleagues of varying technical and non-technical abilities
Curious and always learning habits of mind
Strong team-oriented approach to work, with excellent interpersonal and communication skills, both oral and written
Ability to work effectively as a member of a team in a collaborative environment
Demonstrated ability to manage multiple tasks and projects simultaneously
Experiences That Will Set You Apart
Advanced degree in a quantitative field (CS, EE, statistics, math, data science)
Track record of producing machine learning models and production infrastructure at scale
Familiarity with traditional natural language processing (NLP) techniques and/or latest advancements in large language models (LLMs), generative AI, active learning and reinforcement learning
Strong experience with machine learning in non-NLP domains
Experience using containerized technologies such as Docker and/or Kubernetes
Applications will be accepted through July 30. This window may be extended depending on business needs.
Compensation at Pearson is influenced by a wide array of factors including but not limited to skill set, level of experience, and specific location. As required by the California, Colorado, Hawaii, Illinois, Maryland, Minnesota, New Jersey, New York State, New York City, Vermont, Washington State, and Washington DC laws, the pay range for this position is as follows:
The full-time salary range for this position is between $130,000 - $160,000
This position is eligible to participate in an annual incentive program, and information on benefits offered is here.
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