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# DATA SCIENTIST

- **Company:** South Carolina Public Service Authority (Inc)
- **Location:** Moncks Corner, SC, United States
- **Experience:** Experienced
- **Salary:** $75,500.0 - $105,210.0
- **Contract:** Permanent contract
- **Skills:** Continuous Integration, Information Engineering, Extract Transform Load (ETL), Python (Programming Language), Machine Learning, NumPy, Backtesting, Tensorflow, Test Data, Feature Engineering, Pytorch, Flask (Web Framework), Large Language Models, Model Validation, Fastapi, Pandas, Scikit Learn, Information Technology, Machine Learning Operations, Software Version Control
- **Published:** August 24, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=507f7adb3840f6bf

## About the Role

* Bachelor's degree in Mathematics, Statistics, Computer Science or related field., * Bachelor's degree in Mathematics, Statistics, Computer Science or related field + 2 years experience as an ETL Developer and/or Data Analyst.
 * Must be proficient in Python (pandas, NumPy, scikit-learn; plus PyTorch/TensorFlow for DL; FastAPI/Flask for services; venv/poetry) and working familiarity with R for analysis when needed.

## Description

This position is responsible for independently designing, implementing, and productionizing advanced analytics solutions that blend machine learning, simulation, and optimization to improve operational decision-making at Santee Cooper. The Data Scientist leads end-to-end delivery of moderately complex projects (e.g., improved load forecasting, asset health surrogate models, scheduling/dispatch optimizers), owns model quality and lifecycle, and partners closely with operations to deploy and monitor models in production with measurable business impact.

Essential Job Tasks:

 * Leads end-to-end development of ML/DL models for forecasting, anomaly detection, classification, or surrogate modeling; evaluate trade offs and select appropriate algorithms.
 * Designs and implement mid complexity optimization models (MILP/heuristics) and integrate them with learned models to support decision workflows (maintenance scheduling, dispatch, demand response).
 * Develops simulation experiments and digital twin style workflows for scenario analysis and capacity planning.
 * Build robust feature engineering pipelines and collaborate with data engineering to productionize datasets (feature stores, incremental pipelines).
 * Applies and extends LLM capabilities for automation tasks (intelligent assistants, summarization, data augmentation) using safe prompt engineering and light fine-tuning where appropriate.
 * Implements MLOps best practices: automated training, model versioning, CI/CD, monitoring, and alerting for drift/performance.
 * Performs rigorous model validation: back testing, cross validation, uncertainty quantification, sensitivity analyses, and test data holding strategies.
 * Documents models, assumptions, and provide reproducible artifacts and dashboards for stakeholders.


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