Lead Specialist, AI Scientist

About the Role 

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. 

What You'll Do 

• 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 consumer learning products 

• Design, develop, and maintain a scalable AI data and delivery architecture to deliver real-time personalized features including proficiency estimates and recommendations 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 will turn 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. 

Expected Results:  

• A pan-Pearson universal knowledge graph made up of interconnected domain graphs (never before done) 

• A shared and scaled AI learner model validated by learners, educators, administrators, and industry benchmarks for trust, speed, quality, and cost optimization that delivers learning proficiency estimates and learning recommendations (never before done) 

• Successful implementation of knowledge graph and learner models delivering business growth across Pearson businesses 

• Design, delivery, and continuous improvement of the data and services architecture required for the above 

Qualifications 

• 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 psychometric modeling, 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 Face Transformers 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 

• Preferred - experience deploying containerized workflows using GitLab CI/CD, Docker, ECS or Kubernetes, and managing cloud infrastructure via AWS CLI and Terraform a plus. 

• Preferred - experienced in building scalable MLOps pipelines for data ingestion, preprocessing, training, and deployment, with orchestration using Airflow and experiment tracking via MLflow a plus. 

Apply now and help shape the future of learning. 

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 minimum full-time salary range is between $150,000 - 180,000.  

This position is eligible to participate in an annual incentive program, and information on benefits offered is here.  

Applications will be accepted through July 19th. This window may be extended depending on business needs.