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

About the role

We are looking for a 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

Pay

• Base salary range: $167,100.00 - $226,100.00 USD annually (USA, WA, Seattle)
• Additional compensation includes sign-on payments and restricted stock units (RSUs)
• Final compensation determined based on factors including experience, qualifications, and location

Benefits

• Health insurance (medical, dental, vision, prescription)
• Basic Life & AD&D insurance and option for Supplemental life plans
• Employee Assistance Program (EAP), Mental Health Support, Medical Advice Line
• Flexible Spending Accounts
• Adoption and Surrogacy Reimbursement coverage
• 401(k) matching
• Paid time off
• Parental leave

This response is AI-generated, for reference only.