Learning Scientist - Education Intelligence

Company Overview

tmrw education is developing an education-intelligence division within its AI-driven platform designed to unify fragmented school systems into one intelligent ecosystem. This platform supports global education networks by integrating school operations and educational insights, allowing educators and administrators to focus more on learning and less on administrative tasks. The company prioritizes thoughtful innovation, user-centered design, and practical tools suitable for real-world schools.

Role Summary

The Learning Scientist serves as a critical link between learning science and artificial intelligence, transforming raw educational data into meaningful, reliable intelligence. This position involves defining educational constructs such as strengths, risks, and readiness, and determining how various data points interplay to emulate expert educator reasoning within the platform. The role emphasizes ensuring that AI-driven recommendations are evidence-based, transparent, and aligned with ethical considerations.

Key Responsibilities

• Create clear, valid definitions for educational constructs like student strengths, behaviors, engagement, well-being, growth, and support requirements to enable AI-driven reasoning.
• Identify which datasets should be combined as cohesive signals (e.g., combining attendance, task completion, and well-being into a learning reliability indicator) while ensuring unrelated data remain distinct.
• Develop the semantic learning model by mapping various school data sources into standardized meaning categories to enhance interpretability.
• Design signal 'recipes' that specify the combination and nature of evidence needed to establish signals such as strengths, risks, growth, readiness, and support needs, leaving scoring and calibration to Data Science.
• Establish trust rules, defining the quality and weight of evidence required for defensible inferences and when human review is necessary.
• Conduct validation of constructs and their explanations through engagement with teachers, school leaders, and learners to ensure practical applicability and clarity.
• Collaborate with specialists in Responsible AI and safeguarding to mitigate risks such as bias, excessive inference, or inaccurate signals within partner and GEMS schools.
• Maintain reusability of intelligence by ensuring the educational meanings defined are consistently applied across the Learner360 platform, benefiting teachers, parents, and students.

Role Integration

This position works closely with multiple teams, including:

• Data Science & AI/ML - to transform defined constructs into computational scores and models.
• Data & Ontology Architecture - to incorporate meaning categories into a canonical learner graph and semantic relationships.
• Product, UX & Responsible AI - to prioritize signals and validate that recommendations are fair, transparent, and safe.

Candidate Profile

• Demonstrated experience within education as a teacher, learning scientist, assessment expert, or educational researcher with a deep understanding of how learning manifests in school data.
• Graduate-level expertise in learning science, educational measurement, psychometrics, or cognitive science, with strong judgment regarding valid evidence and inferences.
• Capability to determine appropriate data combinations and articulate these decisions in clear, accessible language.
• Comfort collaborating with data scientists, engineers, and ontology designers without the necessity to engage in coding, but with the ability to guide development.
• Commitment to fairness, safeguarding, and awareness of inference limitations especially concerning children.
• Excellent communication skills that convey complex constructs and confidence thresholds effectively to educators and technical professionals alike.

Preferred Qualifications

• Experience developing rubrics, learning progressions, or competency frameworks.
• Familiarity with typical school data systems such as Student Information Systems (SIS), Learning Management Systems (LMS), markbooks, attendance, well-being, and co-curricular tracking.
• Exposure to knowledge graphs, ontologies, or semantic modelling methodologies.
• Experience working across various curricula or international school networks.

Indicators of Success

• Transforming raw school data into consistent, trusted educational meanings uniformly applied across platforms.
• Delivering evidence-backed, explainable signals that empower educators and leaders to take confident actions.
• Ensuring new agents utilize standardized educational intelligence, enhancing reusability and efficiency.
• Maintaining fair, safe, and defensible learner recommendations suitable for practical application in schools.