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

What you'll do

• Build causal, forecasting, anomaly-detection, and confidence-calibration models for operational metrics and interventions.
• Develop and evaluate LLM reasoning and RAG systems that produce grounded summaries and recommendations.
• Deploy and monitor models, design experiments to assess impact, and communicate findings to senior leaders.

What they ask for

• 3+ years of experience building machine-learning models for business applications.
• PhD in a quantitative field, or Master’s plus 4 years of applied science experience.
• Expertise in at least two of causal inference, time-series forecasting, anomaly detection, and NLP/LLMs.
• Python and ML framework proficiency; experience with experimental and causal methods, production ML deployment, and research contributions.

Nice to have

• LLM integration experience, including prompt engineering, RAG, fine-tuning, or evaluation.
• Experience with dose-response modeling, operational data, Bayesian methods, operations research, or decision-support systems.

Pay

167,100.00 - 226,100.00 USD annually (base salary)

Tools

Python, PyTorch, TensorFlow, scikit-learn, statsmodels

AI focus

Build and deploy operational ML models and LLM-based decision intelligence, including causal analysis, forecasting, anomaly detection, RAG, LLM evaluation, and progressive autonomy.