Senior Data Scientist (hybrid)

Johnson Controls-posted 3 days ago
Full-time • Mid Level
Hybrid • Glendale, IL
5,001-10,000 employees
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Johnson Controls is a global leader in smart, healthy, and sustainable buildings, serving customers in more than 150 countries. We create intelligent buildings, efficient energy solutions, integrated infrastructure, and next-generation transportation systems. Johnson Controls is seeking a Senior Data Scientist with a strong practical background in deploying production-ready AI/ML solutions. This role focuses on developing advanced agentic AI systems, time series analytics, and signal processing capabilities to optimize our building technologies, HVAC systems, and industrial IoT platforms. This is a hybrid position (onsite 3 days per week) based in Glendale, WI. Candidates must be commuting distance, or able to relocate.

  • Design and deploy agentic AI systems that autonomously optimize building operations, energy consumption, and equipment performance
  • Develop and implement advanced time series forecasting models for energy demand, equipment behavior, and operational patterns
  • Apply signal processing techniques to analyze sensor data, detect anomalies, and extract meaningful patterns from noisy industrial environments
  • Build end-to-end machine learning pipelines from data ingestion through model deployment and monitoring in production systems
  • Lead predictive maintenance initiatives using ML models to forecast equipment failures and optimize maintenance schedules
  • Collaborate with engineering and operations teams to translate business problems into practical data science solutions
  • Mentor junior data scientists and establish best practices for model development and deployment
  • Bachelor's degree in Data Science, Computer Science, Engineering, Statistics, or related field
  • 7+ years of professional experience developing and deploying ML/AI solutions in industrial, IoT, or similar environments
  • Experience delivering at least 2-3 production ML models with measurable business impact
  • Hands-on experience building agentic AI systems or autonomous decision-making algorithms
  • Knowledge of reinforcement learning, multi-agent systems, or autonomous optimization frameworks
  • Exposure to LLM-based agents, tool use, or reasoning frameworks for decision-making
  • Solid understanding of supervised and unsupervised ML algorithms with deployment experience
  • Experience with time series forecasting using methods like ARIMA, Prophet, LSTM, or similar approaches
  • Hands-on work with seasonal patterns, trend analysis, and time series decomposition
  • Experience applying time series techniques to real-world datasets (sensor data, energy consumption, etc.)
  • Familiarity with handling missing data, outliers, and non-stationary time series
  • Working knowledge of digital signal processing including filtering, FFT, and spectral analysis
  • Experience processing sensor data from industrial equipment (vibration, temperature, pressure, acoustic signals)
  • Ability to implement feature extraction from signal data and apply noise reduction techniques
  • Understanding of frequency domain analysis and pattern detection in signals
  • Strong proficiency in Python with ML libraries (scikit-learn, TensorFlow or PyTorch, XGBoost)
  • Experience with signal processing libraries (scipy.signal, PyWavelets)
  • Working knowledge of time series libraries (statsmodels, Prophet, or tslearn)
  • Experience with at least one cloud platform (Azure preferred, AWS, or GCP)
  • Solid SQL skills and familiarity with data streaming technologies (Kafka, MQTT)
  • Version control with Git and basic MLOps practices
  • Azure Machine Learning Experience with Azure Machine Learning workspace, automated ML, or deployment capabilities
  • Familiarity with Azure ML pipelines, model registry, or managed endpoints
  • Exposure to Azure Databricks, Azure Synapse Analytics, or Azure IoT Hub
  • Basic knowledge of Azure DevOps for CI/CD or model versioning
  • Exposure to genetic algorithms or evolutionary strategies for optimization problems
  • Experience applying evolutionary computation for hyperparameter tuning or feature selection
  • Interest in nature-inspired algorithms and optimization techniques
  • Experience contributing to predictive maintenance projects or failure prediction models
  • Knowledge of remaining useful life (RUL) estimation or anomaly detection in equipment data
  • Understanding of condition-based monitoring concepts
  • Familiarity with maintenance optimization approaches
  • Competitive compensation including base salary and performance bonus
  • Comprehensive benefits package (health, dental, vision, retirement)
  • Professional development budget and learning opportunities
  • Work on innovative AI/ML technology at global scale
  • Collaborative culture with growth-oriented mindset
  • Flexible work arrangements and work-life balance
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