Data Analytics Specialist Sr

TECO Energy•Lutz, FL

1d•Onsite

About The Position

Power up a career with us. Our people are our greatest investments. Be the light to help us keep our customers connected. If you are interested in a career and not just a position, Tampa Electric is the place to be! Tampa Electric offers competitive pay, a comprehensive benefits package and opportunities for growth and development in a friendly and professional work environment. We embrace diversity and the inclusion of all. We believe our differences, unique perspectives and talents are our strengths and integral to the success of our company. We’re honored to serve approximately 780,000 customers across West Central Florida and safely provide them with clean, affordable and reliable electricity. We’ve been doing it for more than 100 years, and there’s so much more ahead. Join our team of energy experts as we build on that legacy through innovation, continued solar investments, cost-effective and sustainable energy solutions all while keeping top-notch customer service at the center of all we do. Tampa Electric is a subsidiary of Emera Inc., a family of energy companies which also includes TECO Peoples Gas and New Mexico Gas Company. Emera provides energy to residential and commercial customers in the United States, Canada, and the Caribbean, with career opportunities available in all of these locations. POSITION CONCEPT: The Sr Data Scientist - Drone Diagnostics plays a critical role in achieving our safety, reliability, resilience and modernization goals for electric grid. Develops and applies advanced analytics, statistics, machine learning and domain-specific knowledge to transform complex utility data (smart meter, grid operational, geospatial and imagery) into actionable insights. Coordinates closely with the Drone and Diagnostics Center to identify grid performance risks and advise on proactive mitigation of equipment, system and operational failures ensuring safety, compliance, reliability, performance, affordability and resilience of the electric grid. Develops the vision for the Drone and Diagnostics Center to provide comprehensive grid performance insights which bridge data engineering, operations, system planning, maintenance, engineering and other grid functions.

Requirements

Bachelor’s degree in Engineering, Data Science, Applied Mathematics or Statistics.
7 years of progressive work experience data science, analytics, machine learning engineering, with at least 2 years in the utility, energy or related sector.
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.

Nice To Haves

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.
Certified Analytics Professional, ESRI GIS Certification, Python for Data Science, Reliability related certification or related certification.
Electric utility engineering and operational, distribution grid asset experience with experience applying data science, analytics with interdisciplinary team of engineers, field operators and Information Technology teams.
Knowledge of the following: Programming: Python, R, SQL.
Tools/Platforms: Databricks, Azure, Snowflake, Copilot.
Data Handling: Extraction, Loading and Transformation (ETL), large dataset manipulation, cloud storage and processing, APIs.
Visualization: Power BI, ESRI Geospatial Analysis.
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.

Responsibilities

Lead the development, implementation and operation of analytics to monitor grid performance. Analytics will include the development and application of statistical and machine learning to detect, track and predict functional equipment and system failures in the distribution grid 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, Geospatial statistical analysis, Event Detection and Classification, Asset and system degradation modeling, causal inference to link asset failure with contributing factors, Statistical Process Control and others.
Lead the development of analytics to achieve safety, compliance, reliability, performance, affordability and resilience goals for the electric grid. These analytics support the prioritization of maintenance in distribution grid and measurement of the impact of maintenance, track prioritized backlog of grid maintenance, capital improvement and operational improvements for stakeholders.
Lead the direction for analytics in the new Diagnostic center 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 grid data (e.g., SCADA, AMI, LiDAR, thermography, weather). This includes the geospatial acquisition of asset condition from patrols, Remote Sensing, the collection, processing and the application of Analytics on this data to inform work and operational changes.
Lead development of analytics functions for Distribution grid models. This includes coordination with other electric system modeling stakeholders (Planning, DCC, DEO and others) to represent (computer model) the design/expected performance of the electric grid. The Sensing function is the establishment of requirements and the development of Sensing systems to measure the performance of the grid, this includes the communication, networking, storage and Analytics required to visualize the performance of the electric grid.
Lead development of analytics for the support of Storm operations with a multi-year, multi-department roadmap to develop an improved estimation of expected damage, tracking of actual damage and outages in the grid, optimization of restoration, tracking of resources supporting restoration.
Help define and execute the diagnostic center’s data strategy, integrating statistical analysis and predictive modeling into grid reliability programs.
Communicate and present to data science and utility operations teams, ensuring analytical outputs directly support operational objectives and reliability standards.
Communicate findings and recommendations to grid owner/operators, senior leadership, regulatory stakeholders, aligning data-driven insights with business goals.
Mentor others and foster a collaborative environment focused on innovation and technical rigor.