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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr Data Scientist - Energy Supply - **Company:** TECO Energy - **Location:** Lutz, FL, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** JavaScript (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Artificial Neural Networks, Computer Vision, Microsoft Azure, Big Data, Cascading Style Sheets (CSS), Command-Line Interface, Information Systems, Computer Programming, Information Engineering, Extract Transform Load (ETL), Software Debugging, Executive Information Systems, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Microsoft SQL Server, Object Detection, Reliability Engineering, Power BI, Cloud Services, Software Systems, Statistical Process Control (SPC), SQL Databases, Systems Integration, Digital Twin, Esri GIS (Software), Microsoft Power Automate, Snowflake, Random Forest, Model Validation, Data Strategy, Powerquery, Information Technology, Complex Event Processing, Data Analytics, Xgboost, Performance Monitor, Lidar, Data Pipelines, Unsupervised Learning - **Published:** November 12, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=c07df0c4d1a28f88 ## About the Role Required: Bachelor's degree in Engineering, Data Science, Applied Mathematics or Statistics. Preferred: Advanced degree (Master's or PhD) in data science, statistics, engineering and/or information systems representing a blend of data science and ability to apply data science to understand electric grid performance. Graduate course work in Machine Learning/AI, predictive modeling, time-series forecasting, power system reliability or risk analysis. Licenses/Certifications Preferred: Certified Analytics Professional, Python for Data Science, Reliability related certification, etc. Experience Required: 7 years of progressive work experience in data science, analytics, machine learning engineering, with at least 2 years in the utility, energy or related sector. Preferred: Electric utility engineering and operational, asset experience with experience applying data science, analytics with interdisciplinary team of engineers, field operators and Information Technology teams. Knowledge/Skills/Abilities (KSA) Required: Broad knowledge and skills in Analytics and IT/OT system solutions (see preferred below) and a problem-solving mindset with a focus on actionable outcomes. The ability to explain technical findings to non-technical stakeholders and collaborate across operations, engineering and regulatory teams. Preferred: Programming & Development: Python, SQL, JavaScript, HTML/CSS, APIs, CLI, and Automation. Data Engineering: ETL/ELT, data modeling, large dataset processing, API and enterprise system integration, SQL Server, Snowflake, Azure, and cloud data platforms. Analytics & Machine Learning: machine learning, computer vision, statistical and predictive analytics, object detection, image segmentation, anomaly detection, and time-series analysis. Visualization & Geospatial: Power BI, DAX, Power Query, Power Automate, Esri GIS, geospatial analysis, interactive mapping, drone/aerial imagery analytics, executive dashboards, and real-time operational visualization. Software Solutions: full-stack application development, system integration, testing, debugging, and documentation. Knowledge of statistical models (e.g., regression, time series forecasting, survival analysis, Bayesian methods) to detect patterns, estimate probabilities of failure, and quantify uncertainty in grid performance. Development of digital twins and grid simulations, using statistical models to calibrate, validate, and interpret electric grid behavior. Ability to apply hypothesis testing, anomaly detection, and probabilistic risk assessment techniques to evaluate system behavior and identify emerging reliability threats. Ability and strong knowledge of machine learning models (e.g., random forests, neural networks, gradient boosting) that complement statistical analysis for high-dimensional, multi-source grid data such as 3D LiDAR point cloud data and imagery. Ability to lead the design of model validation frameworks, ensuring robust, interpretable, and auditable outputs for operational decision-making. ## Description The Sr Data Scientist - Energy Supply plays a critical role in achieving our safety, reliability, resilience and modernization goals for our generating assets. Develops and applies advanced analytics, statistics, machine learning and domain-specific knowledge to transform complex data (PI Data, WorkMan, GADS, GPIF, PGS Data) into actionable insights. Coordinates closely with the Manager, Asset Management to develop methods to utilize the data available to identify performance risks and develop predictive methods to mitigated equipment, system and operational failures Develops the vision for generation analytics to provide comprehensive performance insights which bridge data, operations, maintenance, engineering., * Lead the development, implementation and operation of Analytics to monitor generation performance. Analytics will include the development and application of statistical and machine learning to detect, track and predict functional equipment and system failures of the generating assets such as: Anomaly Detection utilizing multivariate statistical models and unsupervised learning algorithms, Time Series Analysis and Forecasting, Failure Mode Prediction utilizing classification models, Survival Analysis to estimate time to failure, Event Detection and Classification, Asset and system degradation modeling, causal inference to link asset failure with contributing factors, Statistical Process Control and others. (20%) * Lead the development of analytics using Asset Management and formal reliability engineering to achieve safety, compliance, reliability, performance, affordability and resilience goals for the generating fleet. These analytics support the prioritization of maintenance of the generating assets and measurement of the impact of maintenance, track prioritized backlog of maintenance, capital improvement and operational improvements for stakeholders. (20%) * Lead the vision for analytics at the generating fleet level collaborating closely with Technology (information and operational technology) department to develop data pipelines and architectures that support the acquisition, storage, and processing of large-scale data (e.g., PI, Canary, Workman, GADS, GPIF, PGS Gas Data, Gridmax). This includes the acquisition of asset condition from inspections, reports, and performance data and the application of Analytics on this data to inform work and operational changes. (20%) * Lead development of fleet level analytics centrally located on Power BI to compare performance and review operations at a fleet level. This includes coordination with Data Analytics Platform to coordinate data. (10%) * Lead development of analytics for the support of performance reporting up to and including Net Generation Reporting, outage reporting and planning. (10%) * Guide and oversee the data strategy, and Data Analytics Platform access to provide agentic and semantic tools to the engineering teams. The AI tools should allow the engineering teams at the generating assets to improve performance, reliability, maintenance planning, and long-term capital planning. (10%) * Mentor others and foster a collaborative environment focused on innovation and technical rigor. (10%) SUPERVISION Indirect: May have indirect supervision of project and System governance teams. RELATIONSHIPS Key Internal: Asset Management, Generating Assets, Regulatory, OPMECC, (VP, directors, managers and program leads). Works with IT business relationship manager to prioritize a system portfolio roadmap and coordinate execution of the projects. IT and ES technical leads to develop and implement an effective technology plan. Key External: Consultants (Solution implementers, etc.), Contractors, Industry Associations, Vendors