Data Scientist

• 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.

• 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.

Demonstrates expertise in statistical modeling and machine learning for time-series forecasting and anomaly detection, with a strong focus on building and maintaining ML pipelines and ensuring model performance and interpretability. Proficient in translating business problems into statistical frameworks and communicating complex concepts to stakeholders.

• Statistical Modeling
• Machine Learning
• Python Programming
• Time-Series Modeling
• Databricks ML Ecosystem

• Statistical Methods
• Machine Learning Techniques
• Feature Engineering
• Model Training
• Model Validation
• Model Deployment
• Model Monitoring
• Anomaly Detection
• Predictive Maintenance
• Data Analysis

• Communication
• Problem-Solving

• Utility Networks
• Telemetry Data
• SCADA Data
• Regulatory Compliance
• Grid Operations

• Databricks
• Py Spark
• SQL
• MLflow
• Git