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About This Role
DESCRIPTION
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Do you want to define the multi\-year science vision that transforms how millions of customers experience AWS products? Do you want to influence the AWS investment in Cloud and AI technology and see how your recommendations influencing AWS VP level decisions and driving the growth of AWS business? Do you want to push the boundaries of data science (e.g. statistical modeling, causal inference, econometrics, product growth analytics, and forecasting models) and democratize how AWS senior leaders access analytics insights using agentic analytics system?
The AWS Analytics Engineering is at the forefront of leveraging cutting\-edge AI/ML technology and infrastructure to redefine how AWS product leaders and teams interact with and derive insights from their product and customer data. Our vision is to use data science methods to enable AWS product teams and business leaders to drive product and revenue growth and create personalized, optimized, and simplified product experiences to delight our customers.
We are looking for a customer\-focused Principal Data Scientist to lead and define the science strategy across AWS services. In this role, you will set the technical direction for ML\-driven product analytics across AWS Compute (EC2\), GenAI \& Agents, Database \& Analytics, and Storage (S3\) organizations. You will partner directly with GMs, VPs, and senior product leaders to translate complex business challenges into innovative scientific solutions that directly influence AWS's top line and bottom line. You will analyze underlying product growth insights, understand product growth drivers, and anticipate business risks that need to be surfaced to leadership.
As a Principal Data Scientist, you will be the technical thought leader who becomes a thought partner for senior leaders, hands on analyzing business trends and customer insights, drives cross\-organizational decision alignment, and raises the bar for scientific rigor across the team. You will operate effectively in ambiguous environments, exercise strong business judgment on high\-impact decisions, have high ownership and deep understanding of AWS business, and continuously push the frontier of data science applications at AWS scale.
Key job responsibilities
- Define and drive the multi\-year science vision and data science roadmap for AWS product growth analytics across AWS Compute, Database \& Analytics, Storage, AI/ML, and other organizations
- Attend AWS WBR to answer critical and timely business and analytics questions to drive clarify on AWS’ product growth strategy
- Influence senior leaders across multiple organizations by building mental models on AWS growth and anticipate growth risks that should be mitigated
- Serve as the technical thought leader and strategic advisor to senior AWS leaders (GM/VP level), translating business objectives into high\-impact scientific decisions and identify opportunities that drives overall AWS product and revenue growth
- Establish best practices for decision science, including econometrics, statistical modeling, and causal methods
- Invent, operationalize, and scale novel analytical frameworks and metrics that enable data\-driven product growth and executive decision\-making
- Mentor junior decision scientists, setting the bar for technical quality through code reviews, design reviews, and hands\-on guidance
- Communicate findings, conclusions, and strategic recommendations to both technical and non\-technical executive audiences through effective verbal and written communication
- Identify and champion new science opportunities that expand AAE’s impact across AWS, building the case for investment and driving adoption
A day in the life
As a Principal Data Scientist in AAE org, you will shape the science strategy that underpins product decisions across multiple AWS organizations. You'll spend your time partnering with VPs and GMs to identify the highest\-leverage data science opportunities, architecting novel ML solutions to complex product challenges, and mentoring scientists across the team. You'll drive alignment across cross\-functional stakeholders, ensure scientific rigor in our most critical initiatives, and communicate insights that directly influence AWS product roadmaps and growth strategy. You'll balance long\-term vision\-setting with hands\-on technical leadership, diving deep into model architectures and data pipelines when needed.
About the team
We are a team of scientists and engineers supporting AWS product leaders to make high\-impact decisions through sophisticated analytical frameworks, trusted data science methods, and scalable ML products. We come from diverse backgrounds in statistics, computer science, engineering, and business analytics. We specialize in the full end\-to\-end ML development process, including data ingestion, ETL, model development, and model deployment in production. We support data science needs across AWS EC2, Database \& Analytics, and S3 teams using deep learning, graph neural networks, forecasting, reinforcement learning, causal inference, and more.
High Impact Projects: We work on high\-impact, high\-visibility projects that directly influence AWS product roadmaps and senior leaders' decisions.
Supportive Team Environment: We are proud of our supportive and inclusive team culture, we have each other's back during ups and downs.
Work\-Life Balance: We believe 80% of value comes from 20% of work, so we always prioritize our backlog ruthlessly based on business value.
Learning Opportunity: Extensive opportunities to understand AWS business and leverage state\-of\-the\-art AI/ML and cloud technology.BASIC QUALIFICATIONS
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- Bachelor's degree in engineering, statistics, computer science, mathematics, or a related quantitative field
- 10\+ years of data scientist or similar role involving data extraction, analysis, statistical modeling and communication experience
- Competency in data querying languages (e.g., SQL) and scripting languages (e.g., Python, R)
- Experience with advanced machine learning techniques including deep learning, causal inference, and experimentation systems
- Experience leading large\-scale technical or scientific programs with a proven record of thought leadership and successful delivery
- Track record of influencing senior leadership (Director/VP level) through data\-driven insights and strategic recommendations
PREFERRED QUALIFICATIONS
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- PhD or Master's degree in Computer Science, Statistics, Machine Learning, Economics, Operations Research, or a related quantitative field
- Knowledge of AWS technology stack or Certification
- Experience with Gen AI, large language models, Baysian, Econometrics, or reinforcement learning in applied settings
- Demonstrated ability to build and mentor high\-performing science teams
- Experience establishing measurement frameworks and experimentation systems at scale
- Excellent communication skills with non\-technical executive audiences
- Publication experience at top\-tier conferences or journals
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how\-we\-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign\-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life \& AD\&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, Seattle \- 189,400\.00 \- 256,200\.00 USD annually
Salary Context
This $189K-$256K range is above the 75th percentile for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Amazon.com, 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 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($222K) sits 16% above the category median. Disclosed range: $189K to $256K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Amazon.com AI Hiring
Amazon.com has 122 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, AI Product Manager, AI Software Engineer. Positions span Seattle, WA, US, Santa Clara, CA, US, New York, NY, US. Compensation range: $128K - $338K.
Location Context
AI roles in Seattle pay a median of $228,700 across 516 tracked positions. That's 6% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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