Johnson Controls Data Scientist Interview Questions + Guide in 2025

Johnson Controls is a global leader in building technologies and solutions, committed to creating a more sustainable world through innovative products and services.

As a Data Scientist at Johnson Controls, you will play a critical role in driving product innovation through data analysis and insights. This position involves managing and analyzing data that informs product design and troubleshooting, particularly within the context of chiller and heat pump product development. You will utilize advanced statistical models and software tools to enhance product performance and reliability, while collaborating closely with engineering, design, and manufacturing teams. Strong communication skills are essential, as you will present findings to various stakeholders and contribute to technical documentation for knowledge sharing within the product team. The ideal candidate will possess a deep understanding of data analysis techniques, experience with large datasets, and a keen ability to identify trends and recommend improvements. Additionally, a humble, hungry, and smart approach to teamwork will align well with the company’s core values.

This guide will help you prepare for your interview by providing insights into the skills and mindset needed for success in the role, as well as the types of questions you may encounter during the process.

The interview process for a Data Scientist at Johnson Controls is structured to assess both technical expertise and cultural fit within the organization. It typically consists of several key stages:

1. Initial Screening

The process begins with an initial screening conducted by a recruiter, which usually lasts around 30 minutes. During this call, the recruiter will discuss the role, the company culture, and your background. This is an opportunity for you to articulate your experience in data science, your understanding of product development, and how your skills align with the needs of Johnson Controls.

2. Technical Interview

Following the initial screening, candidates typically participate in a technical interview, which can last up to 90 minutes and is often conducted via video conferencing platforms like Microsoft Teams. This interview usually involves a panel of two interviewers, including a hiring manager and a subject matter expert. The session begins with a brief introduction, after which candidates are presented with theoretical questions related to data science concepts, statistical models, and data analysis techniques.

Candidates should be prepared for a hands-on coding exercise, where they may be asked to manipulate data using tools like Google Colab. For instance, you might be tasked with performing exploratory data analysis (EDA) on a dataset, such as the Titanic dataset, to demonstrate your data manipulation skills and thought process.

3. Onsite Interview

The final stage of the interview process is typically an onsite interview, which may consist of multiple rounds with various team members. These rounds will delve deeper into your technical abilities, including your understanding of data-driven solutions, product performance analysis, and your experience with statistical models relevant to product development. Additionally, expect to engage in discussions about your past projects, how you approach problem-solving, and your ability to communicate findings effectively to cross-functional teams.

Throughout the interview process, candidates should also be prepared to discuss their teamwork skills and how they embody the values of being humble, hungry, and smart, which are essential for success in Johnson Controls' collaborative environment.

As you prepare for your interviews, consider the types of questions that may arise in these discussions.

Here are some tips to help you excel in your interview.

Understand the Technical Landscape

Familiarize yourself with the specific data analysis techniques and statistical models relevant to the products at Johnson Controls, particularly in the context of chillers and heat pumps. Brush up on your knowledge of data manipulation libraries like Pandas, as you may be asked to perform exploratory data analysis (EDA) on datasets during the interview. Being able to articulate your thought process while coding is just as important as getting the right answer, so practice explaining your reasoning as you work through problems.

Prepare for a Collaborative Environment

Johnson Controls emphasizes teamwork and collaboration across various departments. Be ready to discuss your experiences working in cross-functional teams and how you’ve contributed to collective goals. Highlight instances where you’ve successfully communicated complex data insights to non-technical stakeholders, as this will demonstrate your ability to bridge the gap between data science and product development.

Embrace the Company Culture

The company values humility, hunger, and smart collaboration. Prepare to showcase how you embody these traits. Think of examples where you prioritized team success over personal accolades, sought out new opportunities to contribute, or navigated complex interpersonal dynamics. This alignment with the company culture can set you apart from other candidates.

Anticipate a Mixed Interview Format

Expect a combination of theoretical questions and practical coding challenges. Review key concepts such as the bias-variance tradeoff, regularization techniques, and performance metrics like ROC curves and confusion matrices. Additionally, practice coding in environments like Google Colab, as you may be asked to manipulate data directly in a shared notebook.

Communicate Your Continuous Learning Mindset

Johnson Controls is looking for candidates who are committed to ongoing skill development. Be prepared to discuss how you stay current with industry trends, tools, and methodologies. Share any recent projects or courses you’ve undertaken to enhance your data science skills, particularly those that relate to product development and performance evaluation.

Practice Problem-Solving Under Pressure

Given the technical nature of the role, you may face time-constrained coding challenges. Simulate this environment by practicing coding problems with a timer. Focus on articulating your thought process clearly and efficiently, as this will help interviewers understand your approach to problem-solving, even if you don’t arrive at a perfect solution.

By following these tailored tips, you can present yourself as a well-rounded candidate who not only possesses the technical skills required for the Data Scientist role but also aligns with the values and culture of Johnson Controls. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Johnson Controls. The interview process will likely assess your technical knowledge, problem-solving abilities, and communication skills, particularly in relation to product development and data analysis.

Technical Knowledge

1. Explain the Bias-Variance Tradeoff.

Understanding the balance between bias and variance is crucial for model performance.

How to Answer

Discuss how bias refers to the error due to overly simplistic assumptions in the learning algorithm, while variance refers to the error due to excessive complexity in the model. Emphasize the importance of finding a balance to minimize total error.

Example

“The Bias-Variance Tradeoff is a fundamental concept in machine learning. Bias is the error introduced by approximating a real-world problem, which can lead to underfitting, while variance is the error introduced by excessive sensitivity to fluctuations in the training set, leading to overfitting. The goal is to find a model that minimizes both bias and variance to achieve optimal performance.”

2. What is Regularization, and why is it important?

Regularization techniques help prevent overfitting in models.

How to Answer

Explain that regularization adds a penalty to the loss function to discourage complex models. Mention common techniques like L1 (Lasso) and L2 (Ridge) regularization.

Example

“Regularization is a technique used to prevent overfitting by adding a penalty to the loss function. L1 regularization, or Lasso, can lead to sparse models by forcing some coefficients to be exactly zero, while L2 regularization, or Ridge, shrinks coefficients but retains all features. This helps in improving model generalization on unseen data.”

3. Can you explain the ROC Curve and its significance?

The ROC Curve is a graphical representation of a model's performance.

How to Answer

Discuss how the ROC Curve plots the true positive rate against the false positive rate at various threshold settings, and how it helps in evaluating the trade-offs between sensitivity and specificity.

Example

“The ROC Curve, or Receiver Operating Characteristic Curve, is a graphical representation that illustrates the diagnostic ability of a binary classifier as its discrimination threshold is varied. It plots the true positive rate against the false positive rate, allowing us to visualize the trade-offs between sensitivity and specificity, which is crucial for selecting the optimal model threshold.”

4. Describe how you would perform Exploratory Data Analysis (EDA) on a dataset.

EDA is essential for understanding the data before modeling.

How to Answer

Outline the steps you would take, such as data cleaning, visualization, and identifying patterns or anomalies.

Example

“To perform EDA, I would start by cleaning the dataset to handle missing values and outliers. Then, I would use visualizations like histograms, box plots, and scatter plots to understand distributions and relationships between variables. This process helps in identifying trends, patterns, and potential areas for further analysis or feature engineering.”

5. How do you handle missing data in a dataset?

Handling missing data is a common challenge in data analysis.

How to Answer

Discuss various strategies such as imputation, deletion, or using algorithms that support missing values.

Example

“When dealing with missing data, I first assess the extent and pattern of the missingness. Depending on the situation, I might use imputation techniques, such as filling in missing values with the mean or median, or I might choose to delete rows or columns with excessive missing data. In some cases, I may also use algorithms that can handle missing values directly.”

Statistical Analysis

1. What statistical methods do you use for hypothesis testing?

Hypothesis testing is a key aspect of data analysis.

How to Answer

Mention common tests like t-tests, chi-square tests, and ANOVA, and explain when to use each.

Example

“I typically use t-tests for comparing means between two groups, chi-square tests for categorical data to assess relationships, and ANOVA when comparing means across multiple groups. The choice of test depends on the data type and the specific hypothesis being tested.”

2. How do you assess the performance of a predictive model?

Model performance evaluation is critical in data science.

How to Answer

Discuss metrics such as accuracy, precision, recall, F1 score, and AUC-ROC, and explain their relevance.

Example

“To assess the performance of a predictive model, I look at various metrics such as accuracy for overall correctness, precision and recall for understanding the balance between false positives and false negatives, and the F1 score for a harmonic mean of precision and recall. Additionally, I consider the AUC-ROC for evaluating the model's ability to distinguish between classes.”

3. Explain the concept of p-value in hypothesis testing.

Understanding p-values is essential for statistical analysis.

How to Answer

Define p-value and its significance in determining the strength of evidence against the null hypothesis.

Example

“The p-value is a measure that helps determine the significance of results in hypothesis testing. It represents the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value indicates strong evidence against the null hypothesis, leading to its rejection.”

4. What is the Central Limit Theorem, and why is it important?

The Central Limit Theorem is a fundamental concept in statistics.

How to Answer

Explain that the Central Limit Theorem states that the distribution of sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution.

Example

“The Central Limit Theorem states that, given a sufficiently large sample size, the distribution of the sample means will approximate a normal distribution, regardless of the original population's distribution. This is crucial because it allows us to make inferences about population parameters even when the population distribution is unknown.”

5. How do you determine if a dataset is normally distributed?

Normality is an important assumption in many statistical tests.

How to Answer

Discuss methods such as visual inspection using histograms or Q-Q plots, and statistical tests like the Shapiro-Wilk test.

Example

“To determine if a dataset is normally distributed, I would first create visualizations like histograms or Q-Q plots to inspect the shape of the distribution. Additionally, I might perform statistical tests such as the Shapiro-Wilk test, which provides a formal assessment of normality.”