Description

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