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About This Role
Lead Specialist, AI Data Scientist
Location: Hybrid, Hoboken
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.
Who we are:
At Pearson, our purpose is simple: to help people realize the life they imagine through learning. We believe that every learning opportunity is a chance for a personal breakthrough. We are the world's lifelong learning company. For us, learning isn't just what we do. It's who we are. To learn more: We are Pearson.
Pearson is an Equal Opportunity Employer and a member of E\-Verify. Employment decisions are based on qualifications, merit and business need. Qualified applicants will receive consideration for employment without regard to race, ethnicity, color, religion, sex, sexual orientation, gender identity, gender expression, age, national origin, protected veteran status, disability status or any other group protected by law. We actively seek qualified candidates who are protected veterans and individuals with disabilities as defined under VEVRAA and Section 503 of the Rehabilitation Act.
If you are an individual with a disability and are unable or limited in your ability to use or access our career site as a result of your disability, you may request reasonable accommodations by emailing [email protected].
Job: Data Engineering
Job Family: TECHNOLOGY
Organization: Higher Education
Schedule: FULL\_TIME
Workplace Type: Hybrid
Req ID: 24600
Salary Context
This $150K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Pearson, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($165K) sits 25% below the category median. Disclosed range: $150K to $180K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Pearson AI Hiring
Pearson has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, Hoboken, NJ, US. Compensation range: $160K - $180K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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