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About This Role
Job Title: Data Scientist, Assessment \& Learning Analytics
Location: Remote
Employment Type: Full\-Time
About Us:
Amira Learning accelerates literacy outcomes by delivering the latest reading and neuroscience with AI. As the leader in third\-generation edtech, Amira listens to students read out loud, assesses mastery, helps teachers supplement instruction and delivers 1:1 tutoring. Validated by independent university and SEA efficacy research, Amira is the only AI literacy platform proven to achieve gains surpassing 1:1 human tutoring, consistently delivering effect sizes over 0\.4\.
Rooted in over thirty years of research, Amira is the first, foremost, and only proven Intelligent Assistant for teachers and AI Reading Tutor for students. The platform serves as a school district’s Intelligent Growth Engine, driving instructional coherence by unifying assessment, instruction, and tutoring around the chosen curriculum.
Unlike any other edtech tool, Amira continuously identifies each student’s skill gaps and collaborates with teachers to build lesson plans aligned with district curricula, pulling directly from the district’s high\-quality instructional materials. Teachers can finally differentiate instruction with evidence and ease, and students get the 1:1 practice they specifically need, whether they are excelling or working below grade level.
Trusted by more than 2,000 districts and working in partnership with twelve state education agencies, Amira is helping 3\.5 million students worldwide become motivated and masterful readers. Job Summary:
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.
Essential Functions:
Statistical Modeling \& Validation
- 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
Data Science \& ML Collaboration
- 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
Research, Communication \& External Engagement
- 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
Continuous Learning Systems
- 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
Qualifications (Education and Experience):
Education \& Experience
- 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)
Statistical \& Analytical Skills
- 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
AI Tools \& Modern Workflows
- 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
Communication \& Domain Knowledge
- 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
Preferred Qualifications:
- 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
Personal Attributes
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- Passionate about leveraging AI to advance literacy and ensure every child becomes a reader
- Strategic thinker balancing innovation with scientific rigor
- Collaborative leader thriving in fast\-paced, mission\-driven environments
- Strong advocate for responsible AI, transparency, and explainability
- Mission\-driven focus on tangible, measured impact for students
Benefits:
- Competitive Salary
- Medical, dental, and vision benefits
- 401(k) with company matching
- Flexible time off
- Stock option ownership
- Cutting\-edge work
- The opportunity to help children around the world reach their full potential
Commitment to Diversity:
Amira Learning serves a diverse group of students and educators across the United States and internationally. We believe every student should have access to a high\-quality education and that it takes a diverse group of people with a wide range of experiences to develop and deliver a product that meets that goal. We are proud to be an equal opportunity employer.
The posted salary range reflects the minimum and maximum base salary the company reasonably expects to pay for this role. Salary ranges are determined by role, level, and location. Individual pay is based on location, job\-related skills, experience, and relevant education or training. We are an equal opportunity employer. We do not discriminate on the basis of race, religion, color, ancestry, national origin, sex, sexual orientation, gender identity or expression, age, disability, medical condition, pregnancy, genetic information, marital status, military service, or any other status protected by law.
Salary Context
This $175K-$235K range is above the 75th percentile for Data Scientist roles in our dataset (median: $166K across 345 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 26,159 AI roles we're tracking, Data Scientist positions make up 2% of the market. At Amira Learning, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $204,700 based on 441 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. Disclosed range: $175K to $235K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Amira Learning AI Hiring
Amira Learning has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in US. Compensation range: $220K - $235K.
Location Context
AI roles in Austin pay a median of $212,800 across 317 tracked positions. That's 16% above the national median.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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