Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Tier-1 brand, mid-level role, metro location, and broad required skillset elevate competition.
Core analytics, MLOps, and Python skills are transferable across industries, though power-plant domain expertise helps.
Explicit 5–8 years requirement plus mandatory Python, ML, MLOps, and production experience increases screening strictness.
Develop and maintain Python-based analytics workflows and scalable pipelines using operational, sensor, and event data from power plant assets.
Apply statistical, machine learning, and time-series analysis techniques to detect anomalies, diagnose equipment issues, and improve power plant reliability and performance.
Create and iterate on dashboards and interactive analytics tools that enable engineering teams to investigate equipment behavior, troubleshoot issues, and enhance product design.
Bachelor's or Master's degree in Engineering, Computer Science, Statistics, Mathematics, Data Science, or related technical field.
5-8 years of experience in analytics, data science, software development, or a related technical role.
Strong programming skills in Python with experience in Pandas, NumPy, SciPy, scikit-learn, Pytorch, Tensorflow.
Experience in developing production-quality analytics workflows, MLOps, CI/CD pipelines, and dashboards or analytics applications.
Experienced in working with large, complex, and noisy real-world industrial datasets, preferably including operational or sensor data.
Proven ability to deliver analytics solutions that drive measurable improvements in operational reliability or performance.
Strong problem-solving skills with capability to translate engineering challenges into practical analytical solutions and collaborate closely with engineering and domain experts.