Strive Health is revolutionizing the kidney care system by identifying at-risk patients early and providing comprehensive, coordinated care. Our mission is to enhance patient outcomes and experiences by leveraging predictive analytics and high-quality interventions.

As a Sr. Machine Learning Engineer at Strive Health, you will play a critical role in developing and deploying scalable MLOps solutions. Your expertise will guide our technology team, from integrating new ML models to optimizing data science processes. This position requires a blend of technical skills in Python, SQL, and cloud-based architecture, along with strong communication abilities to explain complex concepts to stakeholders.

This guide from Interview Query will navigate you through the interview process, offering essential tips and insights. Let's dive in!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Strive Health as a Machine Learning Engineer. Whether you were contacted by a Strive Health recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Strive Health Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the Strive Health hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the Strive Health Machine Learning Engineer role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around Strive Health’s machine learning systems, MLOps, Python, and SQL queries.

For the Machine Learning Engineer role, take-home assignments regarding deploying ML models, managing ETL pipelines, and leveraging AWS cloud services might be given. Apart from these, your proficiency in hypothesis testing, probability distributions, and machine learning expertise may also be assessed during this round.

Depending on the seniority of the position, case studies and similar real-scenario problems may also be assigned.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your day at the Strive Health office. Your technical prowess, including programming and ML modeling capabilities, will be evaluated against the finalized candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the Machine Learning Engineer role at Strive Health.

Quick Tips For Strive Health Machine Learning Engineer Interviews

• Understand the Healthcare Domain: Strive Health operates at the intersection of technology and healthcare. Familiarize yourself with the specific challenges of kidney care and how ML can address these pain points.
• Showcase End-to-End ML Experience: Highlight your experience with the full machine learning lifecycle, from data preprocessing to model deployment and monitoring. Demonstrated experience with MLOps tools and practices will be beneficial.
• Prepare for Behavioral Questions: Strive Health values a collaborative and compassionate approach to work. Be prepared to discuss experiences that illustrate your ability to work as part of a diverse team, overcome challenges, and contribute to meaningful projects in healthcare.

Typically, interviews at Strive Health vary by role and team, but commonly Machine Learning Engineer interviews follow a fairly standardized process across these question topics.

• 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.

What is the average salary for a Machine Learning Engineer at Strive Health?

Q: What does Strive Health aim to achieve? Strive Health is focused on transforming the broken kidney care system. By leveraging comparative and predictive data analytics, we aim to identify at-risk patients early and provide comprehensive, coordinated care. Our goal is to significantly improve patient outcomes and experiences while reducing inpatient utilization and emergent dialysis crashes.

Q: Why should I apply to Strive Health? Strive Health has been recognized with numerous awards for being a top workplace, such as Forbes America's Best Startup Employers and Built In Colorado's Best Places to Work. We prioritize diversity, employee well-being, and professional development. Our benefits include flexible time off, company wellbeing days, and innovative leave packages like parental leave and sabbaticals. We foster a fun, supportive environment with team-building activities and company gatherings.

Q: What are the key responsibilities of the Sr. Machine Learning Engineer position? You will be pivotal in the development of our ML strategy and MLOps solutions. This includes mentoring team members, bringing new AI and ML models into production, designing scalable MLOps tooling, and contributing to cloud-based architecture. You'll need to be able to dig into data and understand business logic to solve complex problems.

Q: What qualifications are required for the Sr. Machine Learning Engineer role? Candidates need at least 5 years of experience in healthcare or technology-related fields, applied machine learning or data science, and agile development approaches. Additionally, you should have experience building, deploying, and maintaining production-level machine learning systems. Expertise in Python and SQL, as well as communication skills to explain technical concepts, are also preferred.

Q: What benefits does Strive Health offer? Strive Health offers a competitive compensation package including health, dental, and vision insurance; a 401k retirement plan with employer match; life and disability insurance; and Health Savings and Flexible Spending Accounts. We also provide generous vacation time, paid holidays, and an annual performance bonus based on individual and company performance.

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!