Job Title: Learning Scientist, Early Literacy
Location: Remote (Can work from anywhere in Unites States)
Who We Are
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. For more information, visit NWEA.org to learn more.
What You'll Do
The Learning Scientist, Early Literacy brings together deep expertise in how young children learn to read and develop as readers and writers with rigorous research methods to design and evaluate theoretically coherent products. The immediate focus of this role is early learning, where you will help build substantive models of how early literacy develops, capturing the skills and understandings students acquire and how they build on one another. These models are encoded as ontologies and knowledge graphs that underpin product development, and you will carry out studies to understand whether those products work for students and teachers in the early grades (PreK-5). While early literacy is the immediate focus, the role offers room to contribute more broadly as priorities evolve, whether across other areas and grade-bands of ELA such as comprehension and writing.
As an Individual Contributor, the Learning Scientist will think flexibly and creatively about research applications within specific product constraints, roll up their sleeves for hands-on work, and clearly communicate ideas and findings to non-experts.
Responsibilities
What You'll Need
Education & Experience
Research & Analytical Skills
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 content learning standards and learning models/learning trajectories in early literacy.
Familiarity with interpreting student performance data.
Experience applying learning sciences to the design or revision of educational environments for diverse student populations.
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.
Communication & Collaboration
Personal Attributes
Physical Requirements
Salary range: 95k - 105k.
Application Deadline: The application window for this position is expected to close on August 2, 2026. We encourage you to apply as soon as possible. The posting may be available past this date but is not guaranteed.
HMH is fully committed to Equal Employment Opportunity and to attracting, retaining, developing and promoting the most qualified employees without regard to race, gender, color, religion, sexual orientation, family status, marital status, pregnancy, gender identity, ethnic/national origin, ancestry, age, disability, military status, genetic predisposition, citizenship status, status as a disabled veteran, recently separated veteran, Armed Forces service medal veteran, other covered veteran, or any other characteristic protected by federal, state or local law. We are dedicated to providing a work environment free from discrimination and harassment, and where employees are treated with respect and dignity. We actively participate in E-Verify.
| Location | Boston, MA (Remote) |
| Salary | $95,000–$105,000 Per Year |