Senior Applied Scientist - AI for Safe Changes Health Gating

Scientific problem formulation and algorithm development Translate service-health and change-safety problems into scientific objectives, hypotheses, datasets, baselines, and success metrics. Develop and evaluate machine-learning, statistical, causal, time-series, or agent-based methods for regression detection, risk estimation, failure attribution, and gating decisions. Compare technical alternatives and explain tradeoffs involving accuracy, interpretability, latency, scale, cost, reliability, and customer risk. Address noisy, sparse, delayed, imbalanced, and distribution-shifted production data. Build representative evaluation datasets from historical, synthetic, simulated, and production-derived data. Design rigorous offline and production evaluations, including baselines, ablation studies, robustness testing, error analysis, and expert or human evaluation where appropriate. Implement algorithms, data-processing components, and evaluation systems in clean, tested, maintainable code. Contribute to design decisions involving performance, reliability, diagnosability, monitoring, privacy, security, compliance, and cost. Support staged deployment, monitoring, post-launch analysis, and iterative improvement. Build reusable tools, libraries, and evaluation methods for additional scenarios. Design appropriate human oversight, fallback behavior, and safeguards for consequential decisions. Independently drive the applied-science workstream for one or more defined health-gating scenarios. Collaborate with engineers, PMs, data scientists, service owners, and partner scientists. Communicate methods, assumptions, limitations, results, uncertainty, and recommendations clearly. Influence technical decisions through experimentation and scientific judgment. Participate in design, code, model, and experiment reviews. Share reusable practices and support peer development through knowledge sharing and mentoring. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. These requirements include but are not limited to the following specialized security screenings: Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. 3+ years experience creating publications (e.g., patents, libraries, peer-reviewed academic papers). Experience presenting at conferences or other events in the outside research/industry community as an invited speaker. 3+ years experience conducting research as part of a research program (in academic or industry settings). 1+ year(s) experience developing and deploying live production systems, as part of a product team. 1+ year(s) experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping. Experience formulating ambiguous real-world problems as machine-learning, statistical, optimization, or experimental problems. Experience implementing and evaluating ML or statistical algorithms Experience with experimental design, baselines, evaluation metrics, statistical reasoning, and error analysis. Experience working with noisy, missing, imbalanced, biased, or distribution-shifted datasets. Ability to communicate technical findings, limitations, and recommendations to cross-functional partners. Experience with anomaly detection, time-series analysis, causal inference, risk modeling, forecasting, ranking, optimization, or decision-making under uncertainty. Experience building or evaluating AI agents, language-model systems, tool-using workflows, or multi-agent systems and agentic loops. Experience developing evaluation datasets, synthetic data, simulations, golden sets, or human-evaluation processes. Experience with cloud telemetry, observability, CI/CD, incident management, deployment safety, or distributed systems. Contributions demonstrated through shipped systems, patents, publications, open-source work, or substantial internal technical impact. Familiarity with Responsible AI, privacy, security, and compliance requirements.