Strive Health is dedicated to transforming the kidney care system by providing innovative, patient-centered solutions that improve the lives of those affected by kidney disease.

As a Data Scientist at Strive Health, your primary responsibility will be to leverage predictive analytics and advanced statistical modeling to enhance the effectiveness of intervention programs. You will build and implement machine learning algorithms that help identify and prioritize at-risk patients, ultimately contributing to better health outcomes and reduced healthcare costs. Proficiency in programming languages like Python or R, along with a strong background in statistics, machine learning, and data analysis, will be crucial for success in this role. Additionally, being able to collaborate effectively with cross-functional teams, including product and operations, will be essential as you integrate your models into clinical workflows. A passion for continuous learning in the field of data science, particularly in areas such as time series forecasting and natural language processing, will help you thrive in a fast-paced, innovative environment.

This guide aims to equip you with the knowledge and confidence needed to excel in your interview for the Data Scientist position at Strive Health, enabling you to showcase your technical expertise and alignment with the company’s mission.

The interview process for a Data Scientist role at Strive Health is designed to assess both technical expertise and cultural fit within the organization. The process typically unfolds in several structured stages:

1. Initial Screening

The first step is an initial screening, which usually takes place via a phone call with a recruiter. This conversation lasts about 30 minutes and focuses on your background, skills, and motivations for applying to Strive Health. The recruiter will also provide insights into the company culture and the specific expectations for the Data Scientist role, ensuring that you understand how your experience aligns with Strive's mission to transform kidney care.

2. Technical Assessment

Following the initial screening, candidates will undergo a technical assessment, which may be conducted through a video call. This stage typically involves a data science professional who will evaluate your proficiency in statistical modeling, machine learning, and programming languages such as Python or R. You may be asked to solve problems related to predictive analytics, time series forecasting, and algorithm development, reflecting the core responsibilities of the role.

3. Onsite Interviews

The onsite interview process 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 engage with various team members, including data scientists, product managers, and operations personnel. The focus will be on your ability to apply data science techniques to real-world healthcare challenges, as well as your collaboration skills and how you can contribute to Strive's mission of improving patient outcomes.

4. Final Interview

The final interview often involves meeting with senior leadership or key stakeholders within the organization. This stage is crucial for assessing your alignment with Strive Health's values and culture. You may discuss your vision for leveraging data science in healthcare and how you can contribute to the company's strategic goals. This is also an opportunity for you to ask questions about the company's future direction and your potential role within it.

As you prepare for these interviews, it's essential to be ready for a variety of questions that will test your technical knowledge and your ability to work collaboratively in a healthcare setting.

Strive Health Data Scientist Interview Questions

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Strive Health. The interview will focus on your ability to leverage predictive analytics, statistical modeling, and machine learning techniques to improve patient outcomes in the healthcare sector. Be prepared to discuss your technical skills, problem-solving abilities, and how you can contribute to Strive Health's mission of transforming kidney care.

Machine Learning

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

Understanding the distinction between these two types of learning is fundamental in data science, especially in healthcare applications where patient data can be complex.

How to Answer

Discuss the definitions of both supervised and unsupervised learning, providing examples of each. Highlight how these methods can be applied in healthcare settings, such as predicting patient outcomes or clustering similar patient profiles.

Example

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

2. Describe a machine learning project you have worked on. What was your role?

This question assesses your practical experience and ability to contribute to projects at Strive Health.

How to Answer

Detail your specific contributions to the project, the challenges faced, and the outcomes achieved. Emphasize collaboration with cross-functional teams, especially in a healthcare context.

Example

“I led a project to develop a predictive model for identifying patients at risk of acute kidney injury. My role involved data preprocessing, feature selection, and model training using Python. Collaborating with healthcare professionals, we successfully reduced emergency admissions by 20% through timely interventions.”

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

Handling missing data is crucial in healthcare analytics, where incomplete records can skew results.

How to Answer

Discuss various techniques for dealing with missing data, such as imputation, deletion, or using algorithms that can handle missing values. Provide context on when to use each method.

Example

“I typically assess the extent of missing data first. For small amounts, I might use mean imputation, but for larger gaps, I prefer predictive imputation methods or even using models that can handle missing values directly, ensuring that the integrity of the dataset is maintained.”

4. What is your experience with deploying machine learning models in a cloud environment?

This question evaluates your technical skills and familiarity with modern deployment practices.

How to Answer

Explain your experience with cloud platforms, the tools you used for deployment, and any challenges you faced during the process.

Example

“I have deployed several machine learning models using AWS Sagemaker. I set up CI/CD pipelines for automated deployment and monitoring, which allowed us to quickly iterate on model improvements while ensuring reliability in production.”

5. Can you discuss a time when you had to explain a complex model to a non-technical audience?

Communication is key in a healthcare setting where stakeholders may not have a technical background.

How to Answer

Share an example where you simplified complex concepts, focusing on the importance of the model and its implications for patient care.

Example

“I once presented a predictive model for patient readmission to a group of healthcare providers. I used visual aids to illustrate the model’s predictions and focused on how it could help them identify at-risk patients, ensuring they understood the practical benefits rather than the technical details.”

Statistics & Probability

1. Explain the concept of p-value and its significance in hypothesis testing.

Understanding statistical significance is crucial for making data-driven decisions in healthcare.

How to Answer

Define p-value and its role in hypothesis testing, and discuss its implications in the context of healthcare research.

Example

“A p-value indicates the probability of observing the data, or something more extreme, if the null hypothesis is true. In healthcare, a low p-value suggests that the observed effect is statistically significant, which can guide treatment decisions.”

2. How would you assess the effectiveness of a new treatment using statistical methods?

This question tests your ability to apply statistical analysis in a healthcare context.

How to Answer

Outline a structured approach, including defining metrics, selecting appropriate statistical tests, and interpreting results.

Example

“I would start by defining key performance indicators for the treatment’s effectiveness, then use a randomized controlled trial design. After collecting data, I would apply statistical tests like t-tests or ANOVA to compare outcomes between treatment and control groups, ensuring robust conclusions.”

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

This fundamental statistical concept is essential for understanding sampling distributions.

How to Answer

Explain the theorem and its implications for making inferences about population parameters based on sample data.

Example

“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. This is crucial in healthcare analytics, as it allows us to make reliable inferences from sample data.”

4. Describe a situation where you used statistical analysis to solve a problem.

This question assesses your practical application of statistical methods.

How to Answer

Provide a specific example, detailing the problem, the statistical methods used, and the outcome.

Example

“I analyzed patient data to identify factors contributing to high readmission rates. By applying logistic regression, I found that certain demographic factors significantly increased risk, which informed targeted interventions and reduced readmissions by 15%.”

5. How do you ensure the validity and reliability of your statistical models?

This question evaluates your understanding of model evaluation and validation techniques.

How to Answer

Discuss methods such as cross-validation, checking for overfitting, and using appropriate metrics to assess model performance.

Example

“I use k-fold cross-validation to ensure my models generalize well to unseen data. Additionally, I monitor metrics like precision, recall, and F1-score to evaluate performance, ensuring that the model is both valid and reliable for clinical applications.”

• 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. Discuss the meaning of these coefficients in the context of the model.

• How would you design a machine learning model to classify major health issues based on health features? You work as a machine learning engineer for a health insurance company. Design a machine learning model that, given a set of health features, classifies whether an individual will undergo major health issues or not.

• What metrics and statistical methods would you use to identify dishonest users in a sports app? You work for a company with a sports app that tracks running, jogging, and cycling data. Formulate a method to identify dishonest users, such as those who drive a car while claiming to be on a bike ride. Specify the metrics you would analyze and the statistical methods you would use to detect athletic anomalies.

• 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?

• Should Square hire a customer success manager or offer a free trial for a new product? Square's CEO wants to hire a customer success manager for a new software product, while another executive suggests offering a free trial instead. What would be your recommendation to get new or existing customers to use the new product?

• 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.

Choosing a role at Strive Health means becoming part of a mission-driven team that is revolutionizing kidney care. Strive Health's commitment to early identification, engagement, and comprehensive coordinated care makes a tangible difference in the lives of kidney disease patients. The accolades from Forbes, Built in Colorado, and others affirm Strive's dedication to excellence, diversity, and employee wellbeing. The Senior Data Scientist position offers the opportunity to lead groundbreaking ML initiatives, engage with cutting-edge technologies, and collaborate with a dynamic, multifaceted team. If you aspire to play a pivotal role in healthcare innovation and are ready to impact patient outcomes significantly, this is the place for you.

For more insights about Strive Health, check out our main Strive Health Interview Guide. We’ve also created interview guides for other roles, 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 data scientist interview 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!