Strive Health is a mission-driven organization focused on revolutionizing kidney care. By leveraging advanced data analytics and a high-touch care team, Strive Health identifies and engages at-risk kidney disease patients, enhancing their care journey from chronic kidney disease to end-stage kidney disease. The company's innovative approach significantly reduces emergent dialysis and inpatient utilization while improving patient outcomes.

As a Senior Data Scientist at Strive Health, you will be integral to this mission, defining machine learning roadmaps, building ML models, and collaborating with cross-functional teams to integrate these models into clinical workflows. This role demands strong technical expertise, including knowledge of ML frameworks, cloud environments, and statistical modeling. With a competitive compensation package and numerous wellbeing and professional development benefits, Strive Health is an excellent place for top talent in healthcare to thrive.

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 Senior Data Scientist. 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 data scientist hiring manager may be 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 Senior Data Scientist role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around data systems, ETL pipelines, and SQL queries.

In the case of data scientist roles, take-home assignments regarding product metrics, analytics, and data visualization are incorporated. Apart from these, your proficiency against hypothesis testing, probability distributions, and machine learning fundamentals may also be assessed during the 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 Senior Data Scientist role at Strive Health.

Quick Tips For Strive Health Data Scientist Interviews

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Strive Health interview include:

• Understand Strive Health's Purpose: Strive Health is deeply committed to transforming the kidney care system. Familiarize yourself with their model and how data science can contribute to achieving their mission.
• Showcase Your Technical Expertise: Be prepared to discuss your experiences with machine learning, statistics modeling, and the specific technologies mentioned in the job description, such as Python, SQL, and cloud environments like AWS.
• Be Data Driven: Strive Health’s data science interviews assess how well you can provide patient-care insights with data science. Brush up on your knowledge of statistics and probability, given these questions can be some of the hardest to solve.

Typically, interviews at Strive Health vary by role and team, but commonly Data Scientist 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. 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.

What is the average salary for a Data Scientist at Strive Health?

Q: What is Strive Health's mission and impact in kidney care?

A: Strive Health is dedicated to transforming the kidney care system by providing comprehensive, coordinated care that spans the entire patient journey. We leverage data and analytics to identify at-risk patients, significantly reducing emergent dialysis occurrences and improving patient outcomes and experience.

Q: Why should I consider applying to Strive Health?

A: Strive Health is recognized as one of America's Best Startup Employers and offers a culture rooted in diversity and inclusion. We provide generous wellbeing offerings, professional development opportunities, and a collaborative environment that encourages innovation. Our team celebrates successes together and fosters a fun, supportive atmosphere.

Q: What are the key responsibilities for the Senior Data Scientist position at Strive Health?

A: As a Senior Data Scientist, you will define the ML roadmap with senior leaders, build health economic statistical models, and integrate ML models into clinical workflows. You'll collaborate with various teams, develop scalable MLOps practices, and provide technical guidance to early ML team members, while staying updated with the latest ML methodologies.

Q: What qualifications are required for the Senior Data Scientist role?

A: The ideal candidate should have an MS/PhD in Data Science, Computer Science, Statistics, or another quantitative field, and a minimum of 4 years of full-time experience building ML, statistical, or analytical products. Preferred candidates will have experience in healthcare, strong communication skills, and proficiency in Python, SQL, GitHub, and deep learning frameworks like PyTorch, TensorFlow, or Keras.

Q: What does Strive Health offer in terms of compensation and benefits?

A: Strive Health offers a competitive salary range of $107,400.00-$134,200.00, along with benefits including health, dental, and vision insurance, a 401k retirement plan with employer match, paid holidays, flexible vacation time, and an annual performance bonus. We also provide wellness programs like Headspace, Carrot Fertility, and Gympass for all employees.

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!