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About This Role
About Amplify
Amplify helps teachers bring delight and rigor to students every day. We have become a leader in K–12 literacy, biliteracy, math, and science by building inspiring teaching and learning experiences based on research. The Amplify Classroom platform combines curriculum, assessment, and supplemental learning into one coherent high\-quality instructional system. A pioneer in education since 2000, Amplify has developed deep relationships in states and districts by partnering with educators to drive implementation quality and improved outcomes. Today, Amplify serves more than 18 million students and teachers across all 50 states and on six continents. For more information, visit Amplify.com.
Job Description Summary:
As an Associate Data Scientist at Amplify, you will join a talented, cross\-functional team dedicated to seeking empirical answers to crucial questions about Amplify’s products. You’ll play an active role in using data to inform product strategy, product efficacy, and drive business outcomes. In doing so, you will directly influence the learning journeys of millions of students, helping to ensure our solutions provide the rigor and delight educators expect.
Reporting to our Senior Data Science Manager, this is an exciting opportunity for a curious, early\-career data scientist to tackle real\-world education problems. We are deeply committed to providing a collaborative environment where you’ll be encouraged to ask questions, experiment, and grow your skillset alongside a team that is passionate about scaling the impact of educational technology.
Essential Responsibilities:
As an Associate Data Scientist in the Product Spoke, you will analyze large\-scale educational data to derive actionable insights that drive product design and improve customer experience. You will collaborate with cross\-functional teams to tackle complex questions, leveraging both traditional analytical methods and emerging AI\-driven workflows.
Key responsibilities include:
- Partner with product managers, researchers, engineers, and content developers to answer complex business and educational questions.
- Develop, evaluate, and maintain statistical models and AI\-enabled analytical solutions..
- Communicate findings and recommendations to technical and non\-technical stakeholders.
- Leverage AI\-powered tools to improve exploratory analysis, feature engineering, and model development while maintaining high standards for code quality and evaluation.
Required Qualifications:
- Bachelor’s degree in a quantitative field (e,g., Data Science, AI Engineering, Computer Science, Economics, Math, Physics, Statistics) or equivalent experience.
- Proficient in using Python for data analysis tasks (data cleaning, manipulation, analysis).
- Experience in SQL for data analysis.
- Experience applying foundational statistical methods, such as regression and hypothesis testing.
- Experience applying AI solutions to a variety of problem domains.
- Experience with software development methodology and protocols, including version control and testing.
- Demonstrated ability to independently research, troubleshoot, and solve open\-ended analytical problems.
- Excellent communication skills in writing and conversation, especially with non\-technical partners.
- Comfortable working independently and collaboratively in a team environment.
Preferred Qualifications:
- Experience training and evaluating the performance of machine learning models leveraging industry standard libraries like scikit\-learn, xGBoost.
- Familiarity with AI/ML frameworks and libraries such as PyTorch, TensorFlow, or Hugging Face.
- Experience building or fine\-tuning applications using Generative AI or Large Language Models.
- Experience working with Snowflake.
- Background in education or educational technology (EdTech)
*Amplify is an Equal Opportunity Employer. Amplify makes employment decisions based on qualifications and merit, and does not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability status, veteran status, or any other legally protected characteristic or status.*
*Amplify is committed to providing reasonable accommodations for qualified individuals with disabilities, including disabled veterans. If you have a disability and need an accommodation in connection with the application or hiring process, please email* *hiringaccommodations@amplify.com**.*
*If you are selected for employment, a background check will be required. As required by state and local laws and district policies, you may be required to provide additional documentation, such as proof of vaccination, or submit to enhanced background screening, such as fingerprinting.*
*Amplify is an E\-Verify participant.*
Compensation Range: $95K \- $110K
Salary Context
This $95K-$110K range is in the lower quartile for Data Scientist roles in our dataset (median: $155K across 226 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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Amplify, 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 $192,890 based on 463 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $120,000. This role's midpoint ($102K) sits 47% below the category median. Disclosed range: $95K to $110K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Amplify AI Hiring
Amplify has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in US. Compensation range: $108K - $110K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. 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: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.
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