Data Scientist, Assessment & Learning Analytics

AMIRA LEARNING INC

9d•Remote

About The Position

We are seeking an exceptional Data Scientist to work at the intersection of applied statistics, data science, and educational analytics. In this role, you will own the quantitative rigor behind our AI-powered literacy platform — designing and validating the statistical models, pipelines, and experiments that determine how millions of students are assessed and supported in learning to read. You will collaborate closely with AI engineers, product managers, and external partners to ensure our measurement systems are statistically sound, interpretable, and continuously improving. This is a high-impact, technically deep role for someone who thinks rigorously about measurement and analytics, and loves building things that matter.

Requirements

Master's or Ph.D. in Statistics, Data Science, Quantitative Social Science, Applied Mathematics, Educational Measurement, or a closely related field
5+ years of hands-on experience applying statistical and data science methods in applied, production settings (educational assessment experience is a strong plus)
Deep expertise in applied statistics: generalized linear models, mixed-effects models, latent variable modeling, Bayesian methods, and survival/longitudinal analysis
Fluency in R and/or Python for statistical modeling, data manipulation, simulation, and reproducible research
Strong foundation in experimental design, causal inference, and A/B testing methodology
Experience with measurement models (e.g., item response models, classical test theory analogs, diagnostic classification models) expressed in statistical terms
Proficiency with NLP, automated speech assessment, or similar AI applications a strong plus
Actively uses AI-assisted coding tools (Cursor, Claude Code, GitHub Copilot, or similar) in your daily workflow — not just familiar with them, but genuinely enthusiastic about and reliant on them
Comfortable iterating rapidly using AI pair-programming, prompt engineering, and code generation to accelerate statistical analysis and pipeline development
Experience building or contributing to data science infrastructure in cloud or distributed environments
Familiarity with version control (Git), reproducible research pipelines, and collaborative code review practices
Exceptional ability to communicate complex quantitative findings to technical and non-technical stakeholders alike
Familiarity with K–12 education data, federal/state assessment regulations, or AI governance frameworks a strong plus
Experience with literacy assessment, oral reading fluency, or early childhood learning data is highly desirable

Nice To Haves

Proficient in Python
Comfortable and proficient in using state-of-the-art AI tools like Claude Code and Cursor in your data analytic and development pipelines
Ability to bridge applied data science implementation and its impact
Exceptional skill communicating complex technical concepts to diverse audiences

Responsibilities

Design and validate models underlying adaptive assessment systems, automated scoring pipelines, and real-time diagnostic feedback
Develop and maintain automation pipelines for evaluating the impact of system changes on downstream score distributions and student classifications
Apply and extend a variety of state-of-the-art statistical models and approaches to estimate student performance, growth, and score trajectories
Develop simulations to evaluate and validate assessment design decisions
Conduct rigorous validity and reliability analyses on data from early reading/literacy assessments
Partner with ML engineers to design experiments validating AI scoring models, including automated speech recognition, NLP-based scoring, and adaptive algorithm performance
Build AI-powered data pipelines and analytical tooling to monitor score quality, flag anomalies, and support continuous improvement of assessment models
Use AI-assisted development tools — including Cursor, Claude Code, and similar platforms — as core parts of your daily workflow; comfort and enthusiasm for these tools is essential
Develop and validate norm-referenced and criterion-referenced score reporting frameworks grounded in statistical best practices
Conduct linking, equating, and comparability studies to ensure consistent score interpretation across years, cohorts, and assessment variants
Translate complex statistical methodology and results into clear, compelling narratives for non-technical audiences including school district leaders and state procurement teams
Contribute to technical reports, white papers, and RFP responses demonstrating statistical rigor and validity evidence
Support ongoing research addressing the unique challenges of AI-powered formative literacy assessment at scale
Oversee data collection frameworks and longitudinal analytical designs that support ongoing model improvement
Monitor assessment system performance across diverse student populations, and produce solutions to alleviate issues of fairness and equity
Collaborate cross-functionally to deliver analytical insights that directly inform product decisions and instructional recommendations