Data Scientist

Honeywell

Advanced Data Scientist at Honeywell focusing on data-driven solutions for utility platforms. Involves statistical modeling, machine learning, and mentoring junior data scientists.

Posted 7/23/2026full-timeRaleigh • North Carolina • 🇺🇸 United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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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.

Highest-signal resume keywords

Statistical ModelingMachine LearningPython ProgrammingTime-Series ModelingDatabricks ML Ecosystem

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Statistical MethodsMachine Learning TechniquesFeature EngineeringModel TrainingModel ValidationModel DeploymentModel MonitoringAnomaly DetectionPredictive MaintenanceData Analysis

Soft Skills

CommunicationProblem-Solving

Tools & Technologies

DatabricksPySparkSQLMLflowGit

Industry Keywords

Utility NetworksTelemetry DataSCADA DataRegulatory ComplianceGrid Operations

Tech Stack

Tools & technologies

NumpyPandasPySparkPythonRayScikit-LearnSparkSQL

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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, NumPy, scikit-learn, statsmodels, 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.

Benefits

Comp & perks

In addition to a competitive salary, leading-edge work, and developing solutions side-by-side with dedicated experts in their fields, Honeywell employees are eligible for a comprehensive benefits package. This package includes employer subsidized Medical, Dental, Vision, and Life Insurance; Short-Term and Long-Term Disability; 401(k) match, Flexible Spending Accounts, Health Savings Accounts, EAP, and Educational Assistance; Parental Leave, Paid Time Off (for vacation, personal business, sick time, and parental leave), and 12 Paid Holidays.