AI/ML Data Scientist

Position Overview

• 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.

Key Responsibilities

• 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, DevOps, 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.

Required Qualifications

• 6+ years of experience in data science or applied machine learning roles.
• Strong communication and stakeholder management skills.
• Strong proficiency in Python (NumPy, Pandas, Scikit-learn, PyTorch/TensorFlow 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.

Preferred Qualifications

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