Senior Applied Scientist, Amazon Global Data Center Ops Central Insight and Analytics Team

Amazon is hiring a Senior Applied Scientist, Amazon Global Data Center Ops Central Insight and Analytics Team in Seattle, United States. It pays $167k-$226k a year and Level rates it ; you can apply on Amazon.

Level's read on this job

On Level's scale, this applied scientist role is Builds AI ●●●●: the job is building AI systems.
The listed pay starts close to the typical applied scientist posting on Level.
Tools and skills the posting cites include PyTorch, TensorFlow and scikit-learn.
Level classifies it as a senior role.
Level lists 20,000+ other open roles at Amazon.

AI in this role

Design and deploy machine learning and LLM-based decision intelligence models for operational data systems.

PyTorchTensorFlowscikit-learnPythonLLMs

prompt-engineeringragllm-integrationfine-tuningai-evaluationnlpapplied-sciencemachine-learningcausal-inferencetime-series-forecastinganomaly-detection

We are looking for an seasoned Applied Scientist to design, build, and deploy the ML/AI models that power our decision intelligence platform. You will work at the intersection of causal inference, time-series forecasting, anomaly detection, and LLM-based reasoning — all applied to real operational problems with measurable business impact.

Key Job Responsibilities

Decision Intelligence Models
- **Causal inference & root cause analysis:** Build models that decompose fleet-wide metric movements into root causes, distinguishing correlation from causation across operational dimensions (site, service, failure mode, time)
- **Dose-response modeling:** Develop models that learn the quantitative relationship between intervention intensity and outcome magnitude
- **Forecasting & projection:** Build time-series models that project metric trajectories under different intervention scenarios, enabling "if we do X, expect Y by date Z" recommendations
- **Anomaly detection & trend identification:** Develop multi-variate anomaly detection that distinguishes signal from noise in noisy operational data, and identifies emerging patterns before they become crises
- **Confidence calibration:** Build and maintain calibrated confidence scores for recommendations, ensuring the system knows what it knows and what it doesn't
- **Outcome attribution:** Design experiments and causal methods to measure the true impact of interventions

LLM Integration & Reasoning
- **Structured reasoning:** Design LLM prompting architectures that reliably transform operational data into executive-quality narrative summaries, decision framings, and recommendation rationales
- **LLM evaluation:** Build evaluation frameworks that measure LLM output quality (accuracy, actionability, calibration) and detect degradation over time
- **RAG systems:** Design retrieval-augmented generation systems that ground LLM outputs in operational data, historical playbooks, and institutional knowledge
- **Progressive autonomy:** Design the trust-calibration system where AI gradually earns expanded authority based on demonstrated accuracy over time

Research & Production
- **End-to-end ownership:** Take models from research through production deployment — you ship, you monitor, you iterate
- **Experimentation:** Design A/B tests and quasi-experiments to validate model improvements and measure business impact
- **Stakeholder communication:** Translate complex scientific results into actionable insights for non-technical senior leaders

Basic qualifications

- 3+ years of building machine learning models for business application experience
- PhD in Machine Learning, Statistics, Computer Science, Operations Research, or related quantitative field (or Master's + 4 years of applied science experience)
- Strong expertise in at least two of: causal inference, time-series forecasting, anomaly detection, NLP/LLMs
- Proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn, statsmodels)
- Experience with experimental design and causal methods (difference-in-differences, synthetic control, instrumental variables, or Bayesian causal inference)
- Experience deploying ML models to production (not just research/notebooks)
- Track record of publications or equivalent internal research contributions

Preferred qualifications

- Experience in building machine learning models for business application
- Experience with LLM integration (prompt engineering, RAG, fine-tuning, evaluation frameworks)
- Experience with dose-response modeling, treatment effect estimation, or pharmacometric-style modeling
- Experience with operational/infrastructure data (time-series at scale, noisy signals, multi-dimensional hierarchies)
- Experience with Bayesian methods (probabilistic programming, uncertainty quantification)
- Background in supply chain optimization, capacity planning, or operations research
- Experience building decision support systems that serve non-technical stakeholders
- Experience measuring GenAI/productivity tools' causal impact on workflows

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 - 167,100.00 - 226,100.00 USD annually

How we rate this

Senior Applied Scientist, Amazon Global Data Center Ops Central Insight and Analytics Team at Amazon rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Senior Applied Scientist, Amazon Global Data Center Ops Central Insight and Analytics Team is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $167k-$226k a year.
The role is based in US, WA, Seattle.
The job was posted on August 25, 2026.
Seniority: senior.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Prompt engineeringRAGLLM IntegrationFine-tuningAI EvaluationNLPApplied ScienceMachine learning

Also common in applied scientist jobs at this level

C++Java Deep learning Reinforcement learning Spark AWS

Pay

This job starts at: $167k
Typical applied scientist job: $169k

This posting starts close to the typical applied scientist job. See applied scientist jobs

Questions you could be asked

How do you structure and test a prompt to get consistent output from a language model?
How would you design a retrieval step so the model answers from real data instead of guessing?
How have you integrated a large language model into a production application?
Walk me through fine-tuning a model: what data did you use, and how did you check the result?
How do you decide that one model's output is better than another's for a given task?

Adapt your resume

List these exact terms on your resume: Prompt engineering, RAG, LLM Integration, Fine-tuning, and AI Evaluation. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

Get your CV reviewed before you apply

Send your CV and the role you want next. Your review arrives by email within 3 days.

Get a free CV review Check your resume with the ATS resume checker