Lead Specialist, AI Scientist

About the position

At Pearson, we are the world’s digital learning company with more than 24,000
employees operating in 70 countries. We lead the education technology industry
in design, service, and innovation. We are committed to bringing life to a
lifetime of learning and to our talented team who make it all possible. By
creating effective, engaging solutions, we provide boundless opportunities for
learners at every stage of their journey around the world. We achieve this
through cutting-edge technology, uncompromising service, and high-quality
products that are engaging and easy to use.

We are seeking a strategic and hands-on AI data scientist to help drive our
learner model research, design, development, and testing efforts. This role will
be knowledgeable on the latest mathematical and data/AI industry research, will
actively participate in the AI and mathematics community, will be work with the
team for designing and implementing best-in-class AI data frameworks, advocating
internally and externally for ethical AI that maximizes learning outcomes, and
will collaborate the engineering team to turn innovative research into
revenue-generating learning features. The ideal candidate can communicate across
data, engineering, and product, passionate about learning efficacy, and able to
quickly and responsibly turn AI innovation into business impact.

This is an opportunity to use AI to have a significant, direct impact on the
success of market-leading learning products and on millions of people all over
the world that seek to enrich their lives through the power of learning.

We are looking for strong critical thinking skills, technical abilities, the
ability to navigate fast-moving AI tools, creative problem solving, persistent
exploration, a drive to understand our business, and a passion to learn,
iterate, and deliver. The successful candidate will be a thought partner to our
customers (product, engineering, marketing, design, etc.) and will assist them
in understanding AI solutions, opportunities, and delivery. This individual will
dig into requirements for new AI capabilities and user-facing features, the data
required to power these solutions, the processes by which we will develop them
at scale, and the optimal technical architecture for continuous scaled training
and delivery using the latest science and technology. The role involves close
interaction with product management, user design groups, data warehouse
developers, data architects, and software development teams. Strong
communication and interpersonal skills are critical, as is a spirit to roll up
your sleeves and persistently navigate ambiguity to relentlessly deliver for
learners.

Pearson is an Equal Opportunity and Affirmative Action Employer and a member of
E-Verify. All qualified applicants, including minorities, women, veterans, and
people with disabilities are encouraged to apply.

Responsibilities
• Research, design, develop, and test Pearson’s bedrock learner model to power
the next generation of pan-business unit AI-driven learning products
• Partner with Product, Engineering, and Design teams to define and implement
AI strategies across higher education courseware and direct to consumerlearning products
• Design, develop, and maintain a scalable AI data and delivery architecture to
deliver real-time personalized features including proficiency estimates andrecommendations upon which we build the future of learning from Pearson
• Using this architecture, develop the customer-facing AI capabilities with
which Pearson can re-invent our courseware business for the AI age
• Test and iterate quickly without sacrificing quality (0 to go-to-market in
<6months)
• Translate product opportunities into clear data requirements
• Build prototypes, articles, and presentations to educate the organization
(including senior executives) on the latest AI innovations and how we willturn them into business growth
• Publish and patent new math, data, and AI inventions
• Collaborate with Data Engineering to ensure clean, reliable data pipelines
and seamless front-end delivery
• Evangelize a data-informed culture across Product and Engineering teams
through education, enablement, and scalable tooling
• Monitor data quality and tracking integrity, proactively identifying and
resolving gaps or anomalies
• Be undaunted by urgency and ambiguity and be persistent in defining
requirements and creative in designing solutions.
• Wrangle and clean data as needed.
• Support fellow data analysts, data scientists, and engineers to solve data
problems, solve customer and product problems with data, govern quality data,and connect data problems with technical solutions.

Requirements
• 5 years developing AI/ML capabilities, including 3+ years in delivering AI/ML
for learning
• Expertise in the mathematical foundations of statistics, machine learning,
numerical optimization, economics, analytics, econometric and psychometricmodeling, recommendation systems, and natural language processing
• Degree in analytical or related science, including PhD (or candidate) in
AI/ML Machine Learning
• Experience designing and developing AI/ML testing, training, deployment, and
maintenance/CI/CD/CT architecture and pipelines
• Ability to interpret business goals and translate them into technical
solutions
• Proficient at making complex data and mathematical concepts understandable
with all levels of product teams and engineering teams
• Persistence in creative problem solving, organization, and time management
• Experience turning research into quick execution that drives business growth
• Experience working very closely with cross-functional product teams and
building strong relationships
• Strong background in machine learning, including Bayesian methods, natural
language processing, and recommendation systems, using tools such as Pandas,NumPy, SciPy, TensorFlow, PyTorch, and NLP libraries like Hugging FaceTransformers and spaCy.
• Proficient in Python, SQL, and Bash/Shell scripting.
• Effectively communicate technical concepts to non-technical audiences and
represent non-technical concepts to technical audiences

Nice-to-haves
• Preferred - experience deploying containerized workflows using GitLab CI/CD,
Docker, ECS or Kubernetes, and managing cloud infrastructure via AWS CLI andTerraform a plus.
• Preferred - experienced in building scalable MLOps pipelines for data
ingestion, preprocessing, training, and deployment, with orchestration usingAirflow and experiment tracking via MLflow a plus.