Learning Scientist (Mathematics)

HMH

22d•$95,000 - $105,000•Remote

About The Position

NWEA® is a division of Houghton Mifflin Harcourt that supports students and educators through research, assessment solutions, policy and advocacy services, professional learning and school improvement services that fight for equity, drive classroom impact and push for systemic change in our educational communities. For nearly 50 years, NWEA has developed innovative pre-K–12 assessments, including their flagship interim assessment, MAP® Growth™ and their reading fluency and comprehension assessment, MAP® Reading Fluency™. As a Learning Scientist, Early Numeracy, you will use your expertise in how young children learn mathematics to build models that represent the development of early math knowledge and skills. You will help identify what students learn, how their understanding grows over time, and how different concepts connect and build on one another. A key part of this role is developing ontologies and knowledge graphs that capture student learning progressions and serve as the foundation for product development. You will also design and conduct research studies to evaluate how effectively our products support teaching and learning for students in PreK-5 classrooms. As an Individual Contributor, you will think creatively about applying learning science research within product constraints, take a hands-on approach to solving problems, and communicate complex ideas and research findings clearly to both technical and non-technical audiences. You will work closely with cross-functional teams to ensure learning science insights are translated into meaningful, evidence-based product experiences for students and educators.

Requirements

A PhD in developmental psychology, mathematics education, curriculum and instruction, cognitive psychology, or a related field with a mathematics education focus.
Expertise in education trends in mathematics, such as grade bands (e.g., K-2, middle school) and areas of study (e.g., classroom technology use, personalized learning).
A strong background in qualitative or quantitative research methods applied to learning sciences studies:
Quantitative track: Experience applying and interpreting quantitative methods, including classical statistical methods, regression, and latent variable models (e.g., longitudinal, multilevel, or item response data), with proficiency in tools such as R or Python.
Qualitative track: Experience conducting, coding, and interpreting qualitative methods (e.g., cognitive interviews, grounded theory, design-based research), with proficiency in tools such as ATLAS.ti, MAXQDA, or NVivo.
Detailed familiarity with mathematics content learning standards and learning models/trajectories across multiple grade bands.
Familiarity with interpreting student performance data.
In-depth understanding of learning models and trajectories in mathematics.
Proficiency with spreadsheet and productivity software (e.g., Microsoft 365), and a willingness to learn new tools and take an AI-first approach to the work.
Stakeholder Engagement: Communication skills (oral, written, and business) necessary to function effectively as a primary contact with internal project teams and to translate research findings for non-experts.
Project Management: Project management skills necessary to lead all learning-sciences aspect of a small-to-medium project, or a portion of a large and complex project.
Process: Comfort following existing processes, including agile processes and tools.
Willing to dig into hands-on, individual-contributor work, including less glamorous stretches, with efficiency and a positive attitude.
Comfortable pivoting as priorities shifts and moving between projects and grade bands as needed.
Curious about other teams and able to build connections across groups to see and serve the bigger picture.
Demonstrates imagination by looking for new ways of seeing, thinking, and working.
Listens, encourages, and respects differences; treats all people fairly, with honesty and transparency, and encourages open communication and feedback.

Nice To Haves

Experience as an early-grades classroom teacher a plus.

Responsibilities

Demonstrates a strong understanding of how students learn and develop mathematical knowledge, including key concepts such as the Standards for Mathematical Practice. Can clearly explain the progression of learning and the skills students need to be successful in math.
Design and develop math learning progressions and knowledge maps that accurately represent how mathematical understanding and skills build over time, incorporating important concepts, relationships, and student learning pathways.
Distinguishes between educational policies, standards, and the specific skills students need to develop.
Translates standards and learning expectations into clear, measurable student skills and competencies.
Understands how changes in academic standards and educational requirements impact skill development and learning progression.
Applies knowledge of evolving standards to ensure learning models and skill frameworks remain accurate and relevant.
Experience in applying either qualitative or quantitative methods to educational data.
Familiarity interpreting student performance data.
Proficiency with spreadsheet and productivity software and willingness to learn new tools.
An AI-first approach to work (willingness and commitment to implementing ethical use of AI to introduce efficiencies in work processes).
Understands business problems and delivers solutions for them.
Communicates clearly and effectively. Raises concerns early and keeps leadership, partners, and stakeholders informed of important updates and challenges.
Works well with people across different teams. Is a reliable, trustworthy team member who contributes positively and follows through on commitments.
Takes initiative and can work independently. Helps plan and manage their own work, prioritizes effectively, and delivers results based on their level of experience and responsibility.