Senior Data Scientist; USC and GC W2

We are seeking a highly experienced Senior Data Scientist to support and enhance our AIOps (Artificial Intelligence for IT Operations) solution. This position plays a critical role in advancing our anomaly detection, root cause analysis, and intelligent automation capabilities across enterprise systems.

The ideal candidate will bring deep expertise in machine learning, statistical modeling, and large‑scale data analysis, with strong hands‑on proficiency in Python and SQL. This individual will drive innovation in operational intelligence by leveraging anomaly detection, causal reasoning, time series modeling, and emerging GenAI techniques.

• Design and implement scalable machine learning models for AIOps use cases including anomaly detection and root cause analysis.
• Develop and optimize advanced anomaly detection algorithms for infrastructure, application, and operational telemetry data.
• Apply causal reasoning frameworks to identify drivers of incidents and operational disruptions.
• Build and deploy time series forecasting and modeling solutions to predict performance degradation and system failures.
• Develop robust data pipelines and analytical workflows using Python and SQL.
• Integrate Generative AI (GenAI) techniques for intelligent summarization, incident triage, knowledge extraction, and automation.
• Collaborate with engineering, Dev Ops, and platform teams to operationalize ML models in production environments.
• Drive continuous improvement of model performance, scalability, and reliability.
• Mentor junior data scientists and contribute to best practices in MLOps and model governance.

• 6+ years of experience in data science or applied machine learning roles.
• Strong communication and stakeholder management skills.
• Strong proficiency in Python (Num Py, Pandas, Scikit‑learn, PyTorch/Tensor Flow or similar).
• Advanced SQL skills for data manipulation and analysis.
• Proven experience in anomaly detection techniques (statistical, ML‑based, deep learning‑based).
• Strong understanding and practical application of causal inference and causal reasoning methodologies.
• Hands‑on experience with large‑scale structured and time series datasets.
• Solid knowledge of time series modeling (ARIMA, Prophet, LSTM, state‑space models, etc.).
• Experience deploying models into production environments.
• Strong analytical thinking and problem‑solving capabilities.

• Experience in AIOps, IT Operations analytics, or observability platforms.
• Exposure to GenAI / LLM‑based solutions for operational intelligence.