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Strong Tier-1 brand, mid-level generalist analytics title, metro location, and broad skillset increase applicant competition.
Core ML and MLOps skills are transferable but industrial sensor and reliability domain experience increases fit sensitivity.
Mandatory 5-8 years plus required Python, ML, MLOps and production analytics experience makes filters stringent.
Develop and deploy Python-based analytics solutions using operational, sensor, and event data from combined cycle power plants to identify equipment anomalies and improve reliability.
Build and maintain scalable batch and near-real-time analytics pipelines, applying statistical, time-series, machine learning, and AI techniques for diagnostics and predictive maintenance.
Create and iterate dashboards and interactive tools that enable engineering teams to investigate equipment behavior, troubleshoot issues, and improve product design.
Bachelor’s or Master’s degree in Engineering, Computer Science, Statistics, Mathematics, Data Science, or related field.
5-8 years of relevant experience in analytics, data science, software development, or related technical role.
Proficient in Python programming, familiar with libraries like Pandas, NumPy, SciPy, scikit-learn, Pytorch, Tensorflow.
Experience with production-quality analytics workflows including MLOps, CI-CD pipelines, and dashboard or analytics application development.
Able to translate complex engineering problems into practical, interpretable analytics solutions that deliver measurable reliability or performance improvements.
Experience working with large, complex, noisy industrial datasets, especially sensor, IoT, or operational data.
Skilled at building maintainable, user-focused solutions and collaborating effectively with engineering stakeholders to align technical outputs with business needs.