Cambia Health Solutions Software Engineer Interview Questions + Guide in 2025

Cambia Health Solutions is a leading health care company dedicated to transforming the health care experience for consumers and providers alike.

As a Software Engineer at Cambia Health Solutions, you will be integral to the development and maintenance of application systems that support health care operations. Your key responsibilities will include programming, debugging, and testing applications in a collaborative team environment, with a focus on delivering efficient and maintainable code. You will also engage in database design and system testing to ensure high-quality outputs. Ideal candidates will possess strong technical skills, particularly in algorithms and programming languages such as Python, and will have experience with machine learning concepts, as these are critical to addressing complex business problems within the health care sector.

Success in this role requires not only technical proficiency but also strong interpersonal and communication skills, enabling you to work effectively with both engineering teams and business partners. A proactive mindset and the ability to adapt to evolving technologies will set you apart as you contribute to improving business processes and enhancing overall system performance.

This guide will equip you with targeted knowledge and insights to excel in your interview for the Software Engineer position at Cambia Health Solutions, helping you to articulate your skills and alignment with the company's mission.

The interview process for a Software Engineer at Cambia Health Solutions is structured to assess both technical and interpersonal skills, ensuring candidates are well-suited for the collaborative environment of the company. The process typically unfolds in several key stages:

1. Initial Screening

The first step involves a phone screening with a recruiter. This conversation is designed to gauge your interest in the role and the company, as well as to discuss your background and relevant experiences. The recruiter may also provide insights into the company culture and the expectations for the position. Be prepared to discuss your technical skills, particularly in programming and software development, as well as your understanding of the health insurance industry.

2. Technical Interview

Following the initial screening, candidates usually participate in one or two technical interviews. These interviews may be conducted via video call and will focus on your coding abilities, problem-solving skills, and knowledge of software engineering principles. Expect to encounter questions related to algorithms, data structures, and specific programming languages such as Python. You may also be asked to solve coding challenges in real-time, so familiarity with coding platforms like CoderPad can be beneficial.

3. Behavioral Interview

In addition to technical assessments, candidates will likely face a behavioral interview with the hiring manager or a panel of team members. This stage aims to evaluate your soft skills, such as communication, teamwork, and adaptability. You may be asked to provide examples of past experiences where you demonstrated these skills, particularly in challenging situations or when working on collaborative projects.

4. Final Interview

The final stage may involve a more in-depth discussion with senior management or team leads. This interview often combines both technical and behavioral elements, allowing you to showcase your comprehensive understanding of software development and your fit within the team. You might also discuss your approach to specific business problems relevant to Cambia's operations, particularly in the context of healthcare technology.

As you prepare for these interviews, it's essential to familiarize yourself with the specific skills and knowledge areas that are critical for success in this role. Next, we will delve into the types of questions you can expect during the interview process.

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

Clarify Expectations Early

Given the mixed experiences candidates have had with the recruitment process, it’s crucial to clarify the expectations for the interview early on. If you receive a coding assessment link, confirm the topics that will be covered. This proactive approach not only demonstrates your initiative but also ensures you are well-prepared for the specific skills they are interested in, such as machine learning concepts and programming.

Master Key Technical Concepts

Focus on mastering algorithms and machine learning principles, as these are critical for the role. Be prepared to discuss topics like overfitting, precision vs. recall, and hyperparameters in detail. Understanding how to preprocess categorical data and handle imbalanced datasets will also be beneficial. Brush up on Python, as it is a significant part of the technical skill set required for this position.

Prepare for Behavioral Questions

Expect behavioral questions that assess your problem-solving abilities and interpersonal skills. Prepare stories that illustrate your experience in delivering bad news, working with teams, and navigating complex situations. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey your thought process clearly.

Understand the Health Insurance Landscape

Since Cambia operates in the health insurance sector, familiarize yourself with industry-specific knowledge. Be ready to discuss topics like deductibles, patient access to care, and the implications of various health policies. This understanding will not only help you answer questions more effectively but also demonstrate your genuine interest in the company’s mission.

Engage with the Interviewers

During the interview, engage with your interviewers by asking insightful questions about their experiences and the team dynamics. This not only shows your interest in the role but also helps you gauge if the company culture aligns with your values. Remember, interviews are a two-way street, and establishing rapport can leave a lasting impression.

Follow Up Thoughtfully

After the interview, send a thoughtful follow-up email thanking your interviewers for their time. Use this opportunity to reiterate your interest in the position and briefly mention a key point from the interview that resonated with you. This small gesture can set you apart from other candidates and keep you top of mind as they make their decision.

By following these tailored tips, you can approach your interview with confidence and a clear strategy, increasing your chances of success at Cambia Health Solutions. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Software Engineer interview at Cambia Health Solutions. The interview process will likely focus on your technical skills, particularly in programming, algorithms, and machine learning, as well as your understanding of the healthcare domain. Be prepared to discuss your experiences and how they relate to the role.

Algorithms

1. What is overfitting, and how can you identify and combat it?

Understanding overfitting is crucial in machine learning, as it affects model performance.

How to Answer

Explain the concept of overfitting, how it can be detected through validation techniques, and methods to mitigate it, such as regularization or cross-validation.

Example

“Overfitting occurs when a model learns the noise in the training data rather than the actual signal, leading to poor performance on unseen data. I identify it by comparing training and validation performance; if the training accuracy is high but validation accuracy is low, overfitting is likely. To combat it, I use techniques like cross-validation and regularization to ensure the model generalizes well.”

2. Can you explain hyperparameters and provide examples of hyperparameters in tree-based algorithms?

This question tests your knowledge of machine learning model tuning.

How to Answer

Define hyperparameters and discuss their role in model performance, providing specific examples related to tree-based algorithms.

Example

“Hyperparameters are settings that govern the training process of a model and are not learned from the data. In tree-based algorithms like Random Forest, examples include the number of trees, maximum depth of each tree, and minimum samples required to split a node. Tuning these hyperparameters can significantly impact the model's accuracy and efficiency.”

3. Describe a time when you had to solve a complex algorithmic problem. What approach did you take?

This question assesses your problem-solving skills and experience with algorithms.

How to Answer

Share a specific example, detailing the problem, your approach, and the outcome.

Example

“In a previous project, I faced a challenge with optimizing a search algorithm for a large dataset. I analyzed the existing algorithm's performance and identified bottlenecks. I then implemented a more efficient algorithm using a binary search approach, which reduced the search time by 50% and improved overall application performance.”

4. How do you approach debugging a complex system?

Debugging is a critical skill for software engineers, and this question evaluates your methodology.

How to Answer

Discuss your systematic approach to identifying and resolving issues in code.

Example

“When debugging a complex system, I start by reproducing the issue to understand its context. I then use logging and breakpoints to trace the flow of execution and identify where things go wrong. Once I pinpoint the problem, I analyze the code and test potential fixes in a controlled environment before deploying the solution.”

Machine Learning

1. What techniques do you use to preprocess categorical data?

This question tests your knowledge of data preprocessing, which is essential for machine learning.

How to Answer

Discuss common techniques for handling categorical data and their importance in model training.

Example

“To preprocess categorical data, I typically use techniques like one-hot encoding or label encoding, depending on the model requirements. One-hot encoding is useful for nominal categories, while label encoding is suitable for ordinal categories. Proper preprocessing ensures that the model can interpret the data correctly and improves overall performance.”

2. Explain the difference between recall and precision. Why is it important to consider both?

This question evaluates your understanding of model evaluation metrics.

How to Answer

Define recall and precision, and explain their significance in the context of model performance.

Example

“Recall measures the ability of a model to identify all relevant instances, while precision measures the accuracy of the positive predictions. It’s important to consider both because a model can have high precision but low recall, leading to missed opportunities, or vice versa. Balancing these metrics is crucial, especially in healthcare applications where false negatives can have serious consequences.”

3. How would you handle an imbalanced dataset?

Imbalanced datasets are common in real-world applications, and this question assesses your strategies for addressing them.

How to Answer

Discuss techniques for managing imbalanced datasets, such as resampling methods or algorithm adjustments.

Example

“To handle an imbalanced dataset, I might use techniques like oversampling the minority class or undersampling the majority class to create a more balanced dataset. Additionally, I could employ algorithms that are robust to class imbalance, such as using weighted loss functions or ensemble methods like SMOTE to generate synthetic samples.”

4. Describe a business case where you had to analyze claims evaluated by auditors. What was your approach?

This question assesses your ability to apply technical skills to real-world business problems.

How to Answer

Outline your analytical approach, including data collection, analysis, and presentation of findings.

Example

“In a previous role, I analyzed claims evaluated by auditors to identify discrepancies. I started by gathering relevant data from our database and cleaning it for analysis. I then used statistical methods to identify patterns and anomalies in the claims. Finally, I presented my findings to stakeholders, highlighting areas for improvement in our claims processing system.”