Strive Health is dedicated to transforming kidney care through innovative technology and comprehensive patient engagement.

As a Machine Learning Engineer at Strive Health, you will play a pivotal role in developing advanced machine learning models and MLOps solutions that enhance kidney care services. Your key responsibilities will include mentoring team members in machine learning practices, overseeing the deployment of AI and ML models, and designing scalable tools to support the data science lifecycle. A strong candidate will possess extensive experience in applied machine learning, particularly in healthcare settings, and should be proficient in Python and cloud-based architectures. Your work will be essential to driving data-driven insights and improving patient outcomes, aligning with the company’s commitment to compassionate care and operational excellence.

This guide will help you prepare for your interview by providing insights into the specific skills and experiences that Strive Health values most in a Machine Learning Engineer.

The interview process for a Machine Learning Engineer at Strive Health is designed to assess both technical expertise and cultural fit within the organization. Here’s what you can expect:

1. Initial Recruiter Call

The first step in the interview process is a 30-minute phone call with a recruiter. This conversation will focus on your background, experience, and motivation for applying to Strive Health. The recruiter will also provide insights into the company culture and the specific expectations for the Machine Learning Engineer role. This is an opportunity for you to express your interest in transforming kidney care through innovative technology.

2. Technical Assessment

Following the initial call, candidates typically undergo a technical assessment, which may be conducted via a coding platform or through a video call. This assessment will focus on your proficiency in algorithms and Python, as well as your understanding of machine learning concepts. Expect to solve problems that demonstrate your ability to apply machine learning techniques to real-world scenarios, particularly in healthcare contexts.

3. Onsite Interviews

The onsite interview consists of multiple rounds, usually around four to five, each lasting approximately 45 minutes. These interviews will include a mix of technical and behavioral questions. You will be evaluated on your ability to design and deploy scalable MLOps solutions, as well as your experience with cloud-based architectures and data processing. Additionally, interviewers will assess your communication skills, particularly your ability to explain complex technical concepts to non-technical stakeholders.

4. Final Interview with Leadership

The final step in the process often involves a conversation with senior leadership or team leads. This interview will focus on your alignment with Strive Health's mission and values, as well as your long-term vision for contributing to the company's goals. You may also discuss your approach to mentoring others in machine learning practices and your strategies for overcoming challenges in the field.

As you prepare for your interviews, consider the specific skills and experiences that will be relevant to the questions you will encounter.

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

Understand the Mission and Values

Strive Health is dedicated to transforming kidney care through innovative solutions. Familiarize yourself with their mission to improve patient outcomes and the specific challenges they face in the healthcare sector. This understanding will allow you to align your responses with their goals and demonstrate your commitment to their vision.

Highlight Relevant Experience

Given the emphasis on machine learning and healthcare, be prepared to discuss your past experiences in these areas. Share specific examples of projects where you applied machine learning techniques to solve real-world problems, particularly in healthcare settings. Highlight your role in deploying production-level ML systems and any experience you have with cloud services like AWS.

Showcase Technical Proficiency

Brush up on your technical skills, especially in Python and machine learning frameworks such as TensorFlow or PyTorch. Be ready to discuss your familiarity with libraries like NumPy and Pandas, as well as your experience with SQL. Prepare to explain complex technical concepts in a way that is accessible to non-technical stakeholders, as communication is key in this role.

Emphasize MLOps Knowledge

Strive Health is looking for someone who can design and deploy scalable MLOps solutions. Be prepared to discuss your understanding of the data science lifecycle, including training, deployment, monitoring, and inference. Share any experiences you have with CI/CD pipelines and how you have implemented MLOps practices in previous roles.

Prepare for Behavioral Questions

Expect questions that assess your tenacity and ability to overcome obstacles. Strive Health values grit and determination, so be ready to share stories that illustrate your problem-solving skills and resilience in challenging situations. Use the STAR (Situation, Task, Action, Result) method to structure your responses effectively.

Engage with Company Culture

Strive Health prides itself on its diverse and inclusive culture. Be prepared to discuss how you can contribute to this environment and support the company’s Employee Resource Groups. Show your enthusiasm for team-building activities and how you value collaboration and community within the workplace.

Ask Insightful Questions

Prepare thoughtful questions that reflect your interest in the role and the company. Inquire about the current challenges the team is facing, the technologies they are using, or how they measure the success of their machine learning initiatives. This will demonstrate your proactive mindset and genuine interest in contributing to Strive Health’s mission.

By following these tips, you will be well-prepared to showcase your skills and fit for the Machine Learning Engineer role at Strive Health. Good luck!

Strive Health Machine Learning Engineer Interview Questions

In this section, we’ll review the various interview questions that might be asked during a Strive Health machine learning engineer interview. The focus will be on your understanding of machine learning concepts, algorithms, and practical applications, particularly in the healthcare domain. Be prepared to discuss your experience with MLOps, data processing, and how you can leverage machine learning to improve patient outcomes.

Machine Learning Concepts

1. Can you explain the difference between supervised and unsupervised learning?

Understanding the fundamental types of machine learning is crucial for any ML engineer.

How to Answer

Discuss the definitions of both supervised and unsupervised learning, providing examples of each. Highlight scenarios where one might be preferred over the other, especially in healthcare applications.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting patient readmission based on historical data. In contrast, unsupervised learning deals with unlabeled data, identifying patterns or groupings, like clustering patients with similar symptoms for further analysis.”

2. Describe a machine learning project you have worked on from start to finish.

This question assesses your practical experience and project management skills.

How to Answer

Outline the project’s objective, the data you used, the algorithms implemented, and the results achieved. Emphasize your role and contributions throughout the project lifecycle.

Example

“I led a project to predict chronic kidney disease progression using patient data. I collected and preprocessed the data, selected relevant features, and implemented a random forest model. The model improved prediction accuracy by 20%, allowing clinicians to intervene earlier.”

3. How do you handle overfitting in your models?

Overfitting is a common challenge in machine learning, and interviewers want to know your strategies for addressing it.

How to Answer

Discuss techniques such as cross-validation, regularization, and pruning. Mention how you would apply these techniques in a healthcare context.

Example

“To combat overfitting, I use cross-validation to ensure my model generalizes well to unseen data. I also apply L1 and L2 regularization to penalize overly complex models, which is particularly important in healthcare to avoid misleading predictions.”

4. What metrics do you use to evaluate the performance of a machine learning model?

Understanding model evaluation is key to ensuring effective machine learning solutions.

How to Answer

Mention various metrics such as accuracy, precision, recall, F1 score, and AUC-ROC, and explain when to use each.

Example

“I typically use accuracy for balanced datasets, but in healthcare, I prioritize precision and recall to minimize false negatives, which could lead to missed diagnoses. For binary classification tasks, I also analyze the AUC-ROC curve to assess the model’s discriminative ability.”

5. How do you ensure the reproducibility of your machine learning experiments?

Reproducibility is vital in both research and production environments.

How to Answer

Discuss the importance of version control, documentation, and using consistent environments for experiments.

Example

“I ensure reproducibility by using version control systems like Git for my code and maintaining detailed documentation of my experiments. I also utilize Docker containers to create consistent environments, which is crucial when deploying models in healthcare settings.”

Algorithms and Data Processing

1. What is your experience with different machine learning algorithms?

This question gauges your familiarity with various algorithms and their applications.

How to Answer

Briefly describe several algorithms you have used, their strengths, and when you would choose one over another.

Example

“I have experience with algorithms like decision trees, support vector machines, and neural networks. For instance, I prefer decision trees for their interpretability in healthcare, while I use neural networks for complex pattern recognition tasks, such as image analysis in radiology.”

2. How do you approach feature selection for your models?

Feature selection is critical for model performance and interpretability.

How to Answer

Discuss techniques you use for feature selection, such as correlation analysis, recursive feature elimination, or domain knowledge.

Example

“I start with correlation analysis to identify highly correlated features, then apply recursive feature elimination to refine my selection. In healthcare, I also consult with domain experts to ensure that selected features are clinically relevant.”

3. Explain how you would preprocess healthcare data for a machine learning model.

Data preprocessing is essential for effective model training.

How to Answer

Outline the steps you take, including handling missing values, normalization, and encoding categorical variables.

Example

“I preprocess healthcare data by first addressing missing values through imputation or removal. I then normalize numerical features to ensure they are on a similar scale and use one-hot encoding for categorical variables, which is crucial for algorithms that require numerical input.”

4. Can you discuss your experience with cloud-based machine learning services?

Cloud services are increasingly used for deploying machine learning models.

How to Answer

Mention specific cloud platforms you have used, the services they offer, and how they benefit machine learning projects.

Example

“I have experience with AWS services like Sagemaker for building and deploying models. It simplifies the process of scaling and managing resources, which is particularly beneficial for handling large healthcare datasets efficiently.”

5. How do you monitor and maintain machine learning models in production?

Model maintenance is crucial for ensuring ongoing performance.

How to Answer

Discuss strategies for monitoring model performance, retraining schedules, and handling model drift.

Example

“I implement monitoring tools to track model performance metrics in real-time. I schedule regular retraining sessions based on data drift and performance degradation, ensuring that the model remains accurate and relevant in the dynamic healthcare environment.”

• How would you interpret coefficients of logistic regression for categorical and boolean variables? Explain how to interpret the coefficients of logistic regression when dealing with categorical and boolean variables.

• How would you design a machine learning model to classify major health issues based on health features? As a machine learning engineer for a health insurance company, design a model that classifies whether an individual will undergo major health issues based on a set of health features.

• What metrics and statistical methods would you use to identify dishonest users in a sports app? For a sports app tracking running, jogging, and cycling data, formulate a method to identify dishonest users. Specify the metrics you would analyze and the statistical methods you would use to detect athletic anomalies indicative of cheating.

• Develop a function str_map to determine if a one-to-one correspondence exists between characters of two strings at the same positions. Given two strings, string1, and string2, write a function str_map to determine if there exists a one-to-one correspondence (bijection) between the characters of string1 and string2.

• Build a logistic regression model from scratch using gradient descent and log-likelihood as the loss function. Create a logistic regression model from scratch without an intercept term. Use basic gradient descent (with Newton's method) for optimization and the log-likelihood as the loss function. Do not include a penalty term. You may use numpy and pandas but not scikit-learn. Return the parameters of the regression.

• Why are job applications decreasing despite stable job postings? You observe that the number of job postings per day has remained stable, but the number of applicants has been steadily decreasing. What could be causing this trend?

• What would you do if friend requests on Facebook are down 10%? A product manager at Facebook informs you that friend requests have decreased by 10%. How would you address this issue?

• How would you assess the validity of a .04 p-value in an AB test? Your company is running a standard control and variant AB test to increase conversion rates on the landing page. The PM finds a p-value of .04. How would you evaluate the validity of this result?

• How would you analyze the performance of a new LinkedIn feature without an AB test? LinkedIn has launched a feature allowing candidates to message hiring managers directly during the interview process. Due to engineering constraints, an AB test wasn't possible. How would you analyze the feature's performance?

• Customer success manager vs. free trial for Square's new product? The CEO of Square's small business division wants to hire a customer success manager for a new software product, while another executive suggests instituting a free trial instead. What would be your recommendation?

• How would you build a fraud detection model using a dataset of 600,000 credit card transactions? Imagine you work at a major credit card company and are given a dataset of 600,000 credit card transactions. Describe your approach to building a fraud detection model.

• How would you interpret coefficients of logistic regression for categorical and boolean variables? Explain how to interpret the coefficients of logistic regression when dealing with categorical and boolean variables.

• How would you tackle multicollinearity in multiple linear regression? Describe the methods you would use to address multicollinearity in a multiple linear regression model.

• How would you design a facial recognition system for employee clock-in and secure access? You work as an ML engineer for a large company that wants to implement a facial recognition system for employee clock-in, clock-out, and access to secure systems, including temporary contract consultants. How would you design this system?

• How would you handle data preparation for building a machine learning model using imbalanced data? Explain the steps you would take to prepare data for building a machine learning model when dealing with imbalanced data.

If you want more insights about the company, check out our main Strive Health Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about Strive Health’s interview process for different positions.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every Strive Health machine learning engineer interview question and challenge.

You can check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!