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IC20 — Data Scientist (VALUE)

Level intent: Independently delivers data science solutions, analytical insights, and AI-enabled capabilities that support VALUE products, customers, and business outcomes. Owns moderately complex data science initiatives from problem definition through implementation and continuous improvement while building deeper specialization in analytics, machine learning, and emerging AI technologies. This role aligns with the IC20 Emerging Specialist level, where individuals work independently, contribute significantly to team outcomes, and continue developing expertise within their domain.

Summary

As a Data Scientist on the VALUE team, you will transform data into actionable insights that drive product strategy, operational excellence, and customer outcomes. You will analyze structured and unstructured data, develop statistical and machine learning solutions, and communicate findings through clear visualizations, reporting, and recommendations.

You will partner closely with Product Managers, Software Engineers, Business Analysts, Data Engineers, and Quality Engineers to identify opportunities where data and AI can improve decision‑making, automate workflows, enhance customer experiences, and create measurable business value.

In addition to traditional data science responsibilities, this role contributes to Pearson's growing use of Artificial Intelligence technologies, including Generative AI, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) solutions. You will help evaluate, develop, and operationalize AI-enabled capabilities while ensuring responsible, secure, and measurable use of AI technologies. The role combines analytical rigor with practical business application and delivery-focused execution.

Key responsibilities AI & Emerging Technology Contributions (40%)

• Contribute to AI-enabled products and operational initiatives across the VALUE portfolio.
• Support experimentation and implementation of Generative AI, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) capabilities.
• Assist in the development and evaluation of prompts, knowledge retrieval strategies, model outputs, and AI-assisted workflows.
• Build and monitor evaluation frameworks that measure AI accuracy, relevance, reliability, latency, and business impact.
• Partner with engineering teams to integrate AI capabilities into production‑ready services and platforms.
• Help establish best practices for responsible AI, model monitoring, governance, transparency, and human oversight.

Statistical Modeling & Machine Learning (30%)

• Develop, validate, and maintain statistical and machine-learning models that support VALUE business objectives.
• Apply predictive analytics, classification, forecasting, clustering, recommendation, and optimization techniques where appropriate.
• Evaluate model performance and continuously refine solutions using measurable outcomes and stakeholder feedback.
• Ensure model quality through testing, validation, documentation, and performance monitoring.

Collaboration (20%)

• Partner with Product Managers and Business Analysts to translate business questions into analytical solutions.
• Collaborate across Engineering, Product, Architecture, and Operations teams to maximize data-driven decision making.
• Effectively communicate technical findings, assumptions, risks, and recommendations to diverse audiences.
• Share knowledge and mentor peers through collaboration, documentation, and technical discussions.

Data Analysis & Insights (10%)

• Analyze large, complex datasets to identify trends, patterns, risks, and opportunities.
• Transform raw data into actionable recommendations that support business and product decisions.
• Develop dashboards, visualizations, reports, and analytical models that communicate effectively to technical and non-technical stakeholders.
• Define metrics, KPIs, and measurement frameworks to evaluate product and business performance.
• Perform exploratory analysis and hypothesis testing to validate assumptions and inform strategic decisions.

Required education and experience

• Bachelor’s degree in data science, Statistics, Mathematics, Computer Science, Engineering, Analytics, or related field, or equivalent practical experience.
• 3+ years of experience in data science, advanced analytics, machine learning, or related analytical roles.
• Demonstrated experience using Python for data analysis, modeling, and automation.
• Experience with statistical analysis, exploratory data analysis, and predictive modeling.
• Experience working within Agile product or engineering teams.

Knowledge, skills, and abilities

• Data analysis, statistical modeling, and machine learning
• Python-based analytics and solution development
• Data visualization and insight communication
• KPI development, measurement frameworks, and business analysis
• Cross-functional collaboration with Product, Engineering, and stakeholders
• Strong problem-solving, critical thinking, and communication skills
• Data quality, governance, and responsible AI practices
• Experience with cloud-based analytics platforms (Azure preferred)
• Generative AI, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG)
• AI evaluation, experimentation, and continuous learning mindset

Success measures

• Delivers accurate, timely, and actionable insights that influence product and business outcomes.
• Produces high-quality analytical work with clear documentation and reproducible methodology.
• Successfully develops and deploys machine learning or AI-enabled solutions that deliver measurable value.
• Demonstrates increasing expertise in statistical analysis, machine learning, and emerging AI technologies.
• Contributes meaningful improvements to data quality, automation, efficiency, or decision‑making processes.
• Builds trusted partnerships across Product, Engineering, and Business stakeholders.
• Effectively communicates complex technical concepts in an understandable and actionable manner.

Leadership behaviors

Customer Centricity Uses data and AI to better understand customer needs and improve customer outcomes.

Raise the Performance Bar Continuously improves analytical rigor, data quality, model performance, and delivery effectiveness.

Exceptional Collaboration for Value Works across disciplines to transform data into business value and product innovation.

Our Leaders Inspire Demonstrates accountability, curiosity, continuous learning, and responsible use of emerging technologies.

Compensation at Pearson is influenced by factors including skill set, experience, and location.

The full-time salary range for this role is $125,000 – $140,000.

This position is eligible to participate in an annual incentive program.

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 TalentExperienceGlobalTeam@grp.pearson.com.