AI Scientist

Pearson•Hoboken, NJ

2d•Hybrid

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.

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 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

Nice To Haves

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.

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 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.