Senior Data Scientist

Johnson Controls•Glendale, AZ

1d•Hybrid

About The Position

Build your best future with the Johnson Controls team As a global leader in smart, healthy and sustainable buildings, our mission is to reimagine the performance of buildings to serve people, places and the planet. Join a winning team that enables you to build your best future! Our teams are uniquely positioned to support a multitude of industries across the globe. You will have the opportunity to develop yourself through meaningful work projects and learning opportunities. We strive to provide our employees with an experience, focused on supporting their physical, financial, and emotional wellbeing. Become a member of the Johnson Controls family and thrive in an empowering company culture where your voice and ideas will be heard – your next great opportunity is just a few clicks away!

Requirements

At least 6+ 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.
Leadership Skills: A Data Science Lead should have strong leadership skills, including the ability to manage a team of data scientists, provide mentorship and guidance, and foster a collaborative and innovative environment.
Business Acumen: A Data Science Lead should have a good understanding of the business needs and objectives of the organization and should be able to align the data science strategy with the overall business strategy.
Communication Skills: A Data Science Lead should have excellent communication skills, including the ability to communicate complex technical concepts to non-technical stakeholders and collaborate with cross-functional teams.
Project Management: A Data Science Lead should have strong project management skills, including the ability to prioritize tasks, manage timelines, and ensure that projects are completed on time and within budget.
Critical Thinking: A Data Science Lead should have strong critical thinking skills, including the ability to identify key business problems and develop data-driven solutions to solve them.
Creativity: A Data Science Lead should 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.

Responsibilities

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.