Back
Tier-1 brand and Bangalore metro increase competition, but seniority and niche oil-and-gas ML reduce applicant density.
Requires oil-and-gas reliability experience and domain standards, making skills less transferable across industries.
Explicit 12+ years, oil-and-gas experience, advanced degree, and specific ML/cloud/tool requirements make shortlisting strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Lead development and deployment of predictive maintenance ML models to forecast equipment failures and optimize schedules for industrial assets.
Build and improve hybrid AI and physics-based digital twins and advanced anomaly detection systems for asset health monitoring.
Provide cross-functional leadership by mentoring data science teams, collaborating with engineers, and influencing executives in oil & gas reliability domain.
PhD or Master’s degree in Data Science, Reliability Engineering, Chemical/Mechanical Engineering, or Applied Mathematics.
Minimum 12 years of industrial analytics experience, including at least 5 years in oil & gas reliability.
Expertise in time-series forecasting, reinforcement learning, graph neural networks; proficiency in Python, R, Scala, Spark, Hadoop, and cloud-native ML platforms (Azure ML, AWS SageMaker, GCP Vertex AI).
Experience with asset reliability standards, risk-based inspection (RBI), HAZOP analytics, and probabilistic reliability modeling.
Senior professional with deep domain expertise combining machine learning and reliability engineering specifically in oil & gas industry.
Operating style includes leading technical innovation with a focus on measurable impact in asset reliability, operational excellence, cost reduction, safety, and digital transformation.
Experienced in strategic cross-functional collaboration and mentoring within large industrial analytics teams.