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.