Applied Scientist, Data Center Decision Intelligence

Amazon Web Services (AWS); Seattle (WA); On-site; USD 167,000 - 226,000; Full time

Benefits offered by this job

Health insurance

RSUs

Sign-on payments

401(k) matching

Paid time off

Parental leave

Job summary

Amazon.com Services LLC in Seattle, WA is seeking a seasoned Applied Scientist to design, build, and deploy ML/AI models powering our decision intelligence platform. You will work at the intersection of causal inference, time-series forecasting, anomaly detection, and LLM-based reasoning to solve real operational problems with measurable business impact.

From research to production, you will own end-to-end model development, run experiments, and present findings to senior leaders.

Qualifications

• 3+ years of building machine learning models for business applications.
• PhD in Machine Learning, Statistics, Computer Science, Operations Research, or related field (or Master's + 4 years of applied science).
• Strong expertise in at least two: causal inference, time-series forecasting, anomaly detection, NLP/LLMs
• Proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn, statsmodels).
• Experience deploying ML models to production and publications or internal research contributions.

Responsibilities

• Causal inference & root cause analysis: Build models to decompose fleet-wide metric movements into root causes.
• Dose-response modeling: Develop models relating intervention intensity to outcome magnitude.
• Forecasting & projection: Build time-series models for scenario-based projections.
• Anomaly detection & trend identification: Develop multi-variate anomaly detection to distinguish signal from noise.
• Confidence calibration: Build calibrated confidence scores for recommendations.
• Outcome attribution: Design experiments to measure true impact of interventions.
• Structured reasoning: Design LLM prompting architectures for narratives and rationales.
• LLM evaluation: Build evaluation frameworks for LLM output quality and degradation.
• RAG systems: Design retrieval-augmented generation grounding LLM outputs to data.
• Progressive autonomy: Design trust-calibration system for expanded AI authority.
• End-to-end ownership: Take models from research through production deployment.
• Experimentation: Design A/B tests and quasi-experiments to validate improvements.
• Stakeholder communication: Translate results into actionable insights for senior leaders.

Skills

Causal inference

Time-series forecasting

Anomaly detection

NLP/LLMs

Python

Education

PhD in ML/Statistics/CS/OR or MS + 4 years

Tools

PyTorch

TensorFlow

scikit-learn

statsmodels

Job description

Amazon.com Services LLC in Seattle, WA is seeking a seasoned Applied Scientist to design, build, and deploy ML/AI models powering our decision intelligence platform. You will work at the intersection of causal inference, time-series forecasting, anomaly detection, and LLM-based reasoning to solve real operational problems with measurable business impact.

From research to production, you will own end-to-end model development, run experiments, and present findings to senior leaders.