Senior Data Scientist

About Johnson Controls

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.

• 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

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
• Agentic AI & Machine Learning
• 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
• Time Series Analysis
• 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
• Signal Processing
• 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, Tensor Flow or PyTorch, XGBoost)
• Experience with signal processing libraries (scipy.signal, PyWavelets)
• Working knowledge of time series libraries (stats models, 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…