Sr Analytics Engineer

Job Description Summary

As a Senior Analytics Engineer on the Fleet Services Analytics team within Gas Power Engineering, you will develop analytics solutions and dashboard applications that help engineering teams identify equipment anomalies, diagnose component issues, and improve the reliability and performance of combined cycle power plants.
In this role, you will work with large-scale operational, sensor, and event data to build Python-based analytics workflows, apply statistical and machine learning techniques, and create intuitive dashboards that support engineering teams in their investigations, customer issue resolution and improving product design. You will collaborate closely with domain experts, engineers, and cross-functional teams to turn complex operational data into actionable insights and scalable solutions.

Job Description

Roles and Responsibilities

• Partner with domain experts, engineering teams, and cross-functional stakeholders to identify high-impact analytics opportunities
• Develop, test, and deploy Python-based analytics solutions using operational, sensor, and event data from power plant assets
• Apply statistical methods, time-series analysis, machine learning techniques and AI to detect anomalies, identify failure patterns, and support diagnostics
• Perform data wrangling, exploratory data analysis, feature engineering, and root cause analysis on complex industrial datasets
• Design, build, and maintain scalable analytics pipelines for batch and near-real-time processing
• Develop dashboards, visualizations, and interactive analytics tools that provide insights for engineers to investigate equipment behavior, analyze trends, and troubleshoot customer issues and improve product design.
• Translate user needs into technical solutions and iterate based on stakeholder feedback to improve usability and business impact
• Validate analytics performance, monitor deployed solutions, and continuously improve model accuracy, robustness, and interpretability
• Follow software development best practices, including version control, testing, code review, and documentation
• Document data sources, methodologies, assumptions, and solution design to support maintainability, reproducibility, and knowledge sharing
• Stay current with advances in anomaly detection, time-series analytics, and applied machine learning and AI, and evaluate their practical use in industrial applications

• Bachelor's or Master's degree in Engineering, Computer Science, Statistics, Mathematics, Data Science, or a related technical field
• 6-8 years of experience in analytics, data science, software development, or a related technical role.
• Strong programming skills in Python, with hands-on experience using libraries such as Pandas, NumPy, SciPy, scikit-learn, Pytorch, Tensorflow and related tools.
• Strong foundation in statistics, probability, data analysis, and time-series methods
• Experience working with large, complex, and noisy real-world datasets
• Experience developing production-quality analytics workflows, MLOps and CI-CD pipelines, dashboards or analytics applications
• Strong problem-solving skills with the ability to translate engineering or business challenges into practical analytical solutions
• Strong written and verbal communication skills, including the ability to explain technical concepts and analytical results to a range of stakeholders

• Experience with anomaly detection, fault detection, diagnostics, predictive maintenance, or reliability analytics
• Experience working with industrial, operational, IoT, or sensor data
• Experience using AI tools like Github Copilot or Claude Code.
• Experience developing dashboards or analytics applications using tools such as Plotly Dash, Streamlit, Tableau, or Power BI
• Familiarity with cloud platforms, CI/CD pipelines, data engineering workflows, or MLOps practices
• Familiarity with combined cycle power plants, rotating equipment, thermal systems, or related industrial domains
• Experience supporting engineering, operations, or reliability teams with data-driven tools
• Exposure to advanced time-series modelling or deep learning methods is a plus where relevant to practical use cases

• Ability to solve ambiguous engineering problems and build practical solutions from first principles
• Strong intuition for identifying meaningful patterns, anomalies, and failure signals in time-series data
• Track record of delivering analytics that drive measurable operational, reliability, or performance improvements
• Ability to develop solutions that are technically strong, interpretable, maintainable, and useful to end users
• Strong collaboration skills and a customer-focused mindset when working with engineering stakeholders
• Passion for learning and applying analytics to solve real-world industrial challenges