Principal Applied Scientist

You'll work with large-scale operational data, current AI models, and engineers who know this infrastructure deeply, on problems that affect billions of users. You will: Build AI-driven automation, including agentic systems, that manages infrastructure capacity and health at runtime. Develop models that detect anomalies and emerging issues in large-scale telemetry, often before they affect customers. Build systems that perform automated root-cause analysis, reasoning across signals, logs, and dependencies to pinpoint why something went wrong. Design automated mitigation that closes the loop from detection to resolution, reducing reliance on manual intervention. Design machine learning and mathematical optimization models that improve the efficiency, reliability, and performance of our services. Take ideas from prototype to production, turning research into durable systems the business depends on. Work with engineering, product, and research teams to set priorities and deliver results. Working knowledge of SQL or a similar query language. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a quantitative field in Physics, Chemistry, Biophysics etc. AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering or a quantitative field in Physics, Chemistry, Biophysics etc. AND 4+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ years related experience (e.g., statistics, predictive analytics, research) These

requirements include but are not limited to the following specialized security screenings: Experience building machine learning or AI solutions in Python (or C#, C++, R), using modern ML frameworks such as PyTorch or scikit-learn, or LLM-based tools and APIs. Ability to explain technical work clearly to non-specialist audiences and to work well across engineering, product, and research teams. Experience with anomaly detection, root-cause analysis, AIOps, or automated incident mitigation. Experience with time-series forecasting. Experience taking ML systems from prototype to production, including pipelines, monitoring, and operating services in the cloud. Experience with large-scale data platforms (for example, Azure Data Lake, Spark, Kusto, or Hive).