Advanced Data Scientist Job Raleigh area,North Carolina USA,IT/Tech

Job Description

We are seeking an Advanced Data Scientist with strong expertise in statistics, mathematical modeling, and applied machine learning to join our SME Analytics engineering team. This role focuses on identifying and developing data-driven solutions to business problems across our connected utilities platform, such as predictive grid reliability, operational intelligence, and scalable solutions to complex utility problems. The ideal candidate brings a quantitative research background (physics, engineering, statistics, math, or similar) combined with production engineering skills and is comfortable bridging rigorous statistical methodology with novel scalable ML/statistical systems.

You will report directly to our Fellow, and you’ll work out of our Raleigh, NC location on a Hybrid, work schedule.

Responsibilities

Key Responsibilities

Design and implement statistical and machine learning models for time-series forecasting, anomaly detection, and asset health scoring across utility networks.
Build and maintain end-to-end ML pipelines on Databricks from feature engineering and model training to validation, deployment, and monitoring in production.
Apply classical statistical methods (GLMs, GAMs, mixed-effects models, Bayesian inference) alongside modern ML techniques (ensemble approaches, network analysis, neural networks) to solve grid operations problems.
Develop predictive maintenance and degradation models for utility infrastructure using telemetry and SCADA data at scale.
Translate ambiguous business problems into well-defined modeling problems with appropriate statistical frameworks - e.g., knowing when a LM/GLM is sufficient and when gradient boosting or deep learning is warranted.
Implement model monitoring, drift detection, and automated retraining workflows to maintain model performance over time.
Contribute to load forecasting, demand response optimization, and outage prediction systems.
Ensure model interpretability and explainability for utility stakeholders and regulatory compliance.
Contribute to internal knowledge-sharing on statistical best practices.

Qualifications

YOU MUST HAVE

Bachelor’s degree or equivalent in Statistics, Applied Mathematics, Physics, Engineering, Data Science, or a related quantitative field.
3+ years of experience (with Bachelor’s), 2+ years of experience (with Masters), or 1+ years (with PhD) in applied statistical modeling and machine learning, with a track record of deployed production models.
Strong programming skills in Python (PySpark, pandas, Num Py, scikit-learn, stats models, XGBoost), R (tidyverse, lme4, glmmTMB, glmnet, mgcv), and SQL for large-scale data analysis.
Experience with time-series modeling (ARIMA, state-space models, LSTM, Darts, or similar) on high-volume meter data.
Exposure to Databricks ML ecosystem (Feature Store, Experiment Track, Model Serving, Mosaic AI) and MLflow.
Familiarity with distributed computing concepts - PySpark, Optuna/Ray, Spark SQL, partitioning strategies, and medallion architecture.
Understanding of software engineering principles - version control (Git), testing, CI/CD for ML systems.
Ability to communicate complex statistical/ML concepts to non-technical stakeholders.

WE VALUE

WE VALUE

Master’s or PhD in Statistics, Applied Mathematics, Physics, Engineering, or a related quantitative field.

Experience in the electric or gas utility industry.
Familiarity with AMI, SCADA, GIS, or OMS data.
Familiarity with deep learning (PyTorch) and geospatial analysis…