Johnson Controls

Software Engineer - Data Scientist

Job description

What You Will Do


At Johnson Controls, we’re shaping the future to create a world that’s safe, comfortable, and sustainable. Our global team creates innovative, integrated solutions to make cities more connected and buildings more intelligent. We are passionate about improving the way the world lives, works and plays. The future requires bold ideas, an entrepreneurial mindset, and collaboration across boundaries.


How You Will Do It


  • Lead a team of Data Scientists to achieve organizational goals
  • Work closely with JCI product teams and product data scientists to create and implement data models for customers.
  • Ability to lead global teams and actively engage with JCI’s top customers.
  • Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
  • Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques, and business strategies.
  • Assess the effectiveness and accuracy of new data sources and data-gathering techniques.
  • Develop custom data models and algorithms to apply to data sets.
  • Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting, and other business outcomes.
  • Develop a testing framework and test model quality.
  • Coordinate with different functional teams to implement models and monitor outcomes.
  • Develop processes and tools to monitor and analyze model performance and data accuracy.


What We Look For


  • At least 3+ years of data science/engineering experience
  • Strong problem-solving skills with an emphasis on product development.
  • Strong experience using statistical computer languages (Python, SLQ, etc.) to manipulate data and draw insights from large data sets.
  • Strong experience working with and creating data architectures.
  • Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
  • Strong practical knowledge of LLM, Reinforcement Learning, Hugging Face, Generative AI, Signal Processing, and Outlier Detection, Bayesian Networks.
  • Communication Skills: A Data Scientist should have excellent communication skills, including the ability to communicate complex technical concepts to non-technical stakeholders and collaborate with cross-functional teams.
  • Critical Thinking: should have strong critical thinking skills, including the ability to identify key business problems and develop data-driven solutions to solve them.
  • Creativity: be able to think creatively and innovatively and develop new approaches to solve business problems using data-driven techniques.
  • Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests, and proper usage, etc.) and experience with applications.
  • Excellent written and verbal communication skills for coordinating across teams.
  • A drive to learn and master new technologies and techniques. Coordinate with different functional teams to implement models and monitor outcomes.
  • Develop processes and tools to monitor and analyze model performance and data accuracy.

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