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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.
 

Honeywell helps organizations solve the world's most complex challenges in automation, the future of aviation and energy transition. As a trusted partner, we provide actionable solutions and innovation through our Aerospace Technologies, Building Automation, Energy and Sustainability Solutions, and Industrial Automation business segments - powered by our Honeywell Forge software - that help make the world smarter, safer and more sustainable.

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, 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.
  

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 (GeoPandas, network/graph-based modeling).
• Familiarity with generative AI and LLM-based solutions as complementary tools.
• Familiarity with the invention disclosure and patent process; ability to write concise technical descriptions of novel methods and systems for intellectual property filings.
  

BENEFITS OF WORKING FOR HONEYWELL

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. Learn more (https://benefits.honeywell.com/)

The application period for the job is estimated to be 40 days from the job posting date; however, this may be shortened or extended depending on business needs and the availability of qualified candidates. Job Posted: July 23, 2026

About Honeywell Technologies

Honeywell Technologies is a global, pure-play automation company with a legacy of innovating to help solve the world's most mission-critical challenges, enhancing the quality of life for people and communities around the world. We serve the building, industrial, and process sectors with a broad portfolio of services, solutions, and products, underpinned by our Honeywell Technologies Accelerator operating system and Honeywell Technologies Forge intelligence layer. By combining the deep domain expertise of our more than 50,000 employees with decades of data from our global installed base, we are uniquely positioned to lead the industrial sector's transition from automation to autonomy.

THE BUSINESS UNIT

Honeywell's Intelligent Automation (IA) Strategic Business Group focuses on delivering innovative automation, control, and software solutions that enhance operational efficiency and safety for customers worldwide. IA integrates advanced technologies and domain expertise to provide scalable and sustainable solutions across diverse industries, driving digital transformation and operational excellence.

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.