Applied Scientist, Data Center Operations Insight and Analytics (Remote)

- 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
- 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

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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
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