Hims & Hers Data Scientist Interview Questions + Guide in 2025

Hims & Hers is a healthcare company focused on providing accessible and stigma-free healthcare solutions through a variety of online products and services.

As a Data Scientist at Hims & Hers, you will play a critical role in leveraging data science and machine learning techniques to enhance various facets of the business, including marketing, product development, and operational processes. You will be expected to collaborate cross-functionally with teams in Analytics, Product, Marketing, and Operations, conducting in-depth analyses to uncover insights that drive impactful business decisions. Key responsibilities include creating data science roadmaps that align with company goals, identifying opportunities for machine learning applications, and guiding the Data Science team in best practices and methodologies.

The ideal candidate possesses a strong technical background with extensive experience in building and deploying machine learning models, as well as excellent communication skills to bridge the gap between technical and non-technical stakeholders. Proficiency in SQL and Python, along with familiarity with machine learning tools such as pandas, sklearn, PyTorch, or TensorFlow, is essential. Furthermore, a robust understanding of statistics and probability will be crucial for deriving meaningful insights from complex datasets. A passion for driving business impact through data science, coupled with a leadership mindset, will position you as a key player in Hims & Hers' mission to revolutionize healthcare accessibility.

This guide will provide you with tailored insights and questions to prepare for your interview effectively, ensuring you can demonstrate both your technical expertise and your alignment with the company’s values and goals.

The interview process for a Data Scientist role at Hims & Hers is structured to assess both technical expertise and cultural fit within the organization. It typically consists of several key stages:

1. Initial Recruiter Call

The process begins with a phone call from a recruiter, lasting about 30-45 minutes. This conversation serves as an introduction to the role and the company, where the recruiter will discuss the job specifics, compensation, and the overall culture at Hims & Hers. They will also inquire about your background, experiences, and motivations to ensure alignment with the company’s values.

2. Technical Screen

Following the initial call, candidates undergo a technical screening, which lasts approximately 45 minutes. This stage may involve a mix of problem-solving scenarios and discussions about your past experiences. Candidates should be prepared to tackle a small problem or redesign a workflow for a new feature, demonstrating their thought process and technical acumen. While coding may not always be required, articulating your approach and reasoning in detail is crucial.

3. Interview Loop

The next phase consists of an interview loop, typically comprising two technical interviews and two behavioral interviews, each lasting around 30-45 minutes. The technical interviews focus on algorithms, data structures, and machine learning concepts, often utilizing platforms like CoderPad for coding exercises. Candidates may encounter medium-level coding questions and should be ready to discuss their previous projects and the impact of their work. The behavioral interviews will assess your ability to communicate effectively and collaborate with cross-functional teams, as well as your approach to problem-solving in ambiguous situations.

4. Final Interviews

In some cases, candidates may have additional interviews with team members or managers, which can include product case studies or discussions about specific metrics and how to influence them. This stage is designed to evaluate your strategic thinking and ability to drive business outcomes through data science initiatives.

As you prepare for your interviews, consider the various skills and experiences that will be relevant to the questions you may face.

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

Understand the Role's Technical Requirements

As a Data Scientist at Hims & Hers, you will be expected to demonstrate a strong command of statistics, algorithms, and machine learning techniques. Prioritize brushing up on your knowledge of statistical methods and probability, as these are crucial for the role. Be prepared to discuss how you have applied these skills in past projects, particularly in areas like marketing optimization and customer segmentation. Familiarize yourself with Python and relevant libraries such as pandas and sklearn, as technical interviews may include coding challenges or problem-solving scenarios.

Prepare for Behavioral Questions

Behavioral interviews at Hims & Hers focus on your past experiences and how they relate to the role. Expect questions that explore your ability to work cross-functionally and communicate effectively with both technical and non-technical stakeholders. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you provide specific examples that highlight your leadership, mentorship, and problem-solving skills. Reflect on times when you had to navigate ambiguity or evolving priorities, as these experiences will resonate well with the interviewers.

Emphasize Your Business Impact

Hims & Hers values candidates who can demonstrate a clear understanding of how data science can drive business outcomes. Be prepared to discuss specific projects where your data-driven insights led to measurable improvements. When asked about your approach to a hypothetical problem, articulate not just the technical solution but also how it aligns with the company's goals and objectives. This will showcase your ability to think strategically and your passion for using data to disrupt the healthcare industry.

Engage with the Interviewers

The interview process at Hims & Hers is designed to be collaborative and friendly. Take the opportunity to engage with your interviewers by asking insightful questions about their experiences and the challenges they face. This not only demonstrates your interest in the role but also allows you to gauge the company culture and team dynamics. Show enthusiasm for the mission of Hims & Hers and how you can contribute to breaking down barriers to healthcare access.

Practice Problem-Solving Scenarios

During the technical interviews, you may encounter problem-solving scenarios that require you to redesign workflows or propose new features. Practice articulating your thought process clearly and concisely, as interviewers will be looking for your ability to think critically and communicate effectively. Prepare to ask clarifying questions to ensure you fully understand the problem before diving into your solution. This will demonstrate your analytical skills and your commitment to delivering high-quality work.

Follow Up with Gratitude

After your interviews, send a thoughtful thank-you note to your interviewers. Express your appreciation for their time and insights, and reiterate your enthusiasm for the role and the company. This small gesture can leave a lasting impression and reinforce your interest in joining the Hims & Hers team.

By following these tips, you will be well-prepared to showcase your skills and fit for the Data Scientist role at Hims & Hers. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Hims & Hers. The interview process will likely focus on your technical expertise in data science, machine learning, and statistics, as well as your ability to communicate complex ideas effectively to both technical and non-technical stakeholders. Be prepared to discuss your past experiences, problem-solving approaches, and how you can drive business impact through data-driven solutions.

Technical Skills

1. Can you describe a machine learning project you have worked on and the impact it had on the business?

This question assesses your practical experience with machine learning and your ability to articulate the business value of your work.

How to Answer

Discuss the project’s objectives, the methodologies you employed, and the measurable outcomes that resulted from your work. Highlight any challenges you faced and how you overcame them.

Example

“I led a project to develop a customer segmentation model that improved our marketing targeting. By using clustering algorithms, we identified distinct customer groups, which allowed us to tailor our campaigns. This resulted in a 20% increase in conversion rates over three months.”

2. How do you approach feature selection for a machine learning model?

This question evaluates your understanding of model performance and the importance of relevant features.

How to Answer

Explain your process for selecting features, including any techniques you use to assess their importance, such as correlation analysis or recursive feature elimination.

Example

“I start by analyzing the correlation between features and the target variable. I also use techniques like recursive feature elimination to iteratively remove less important features. This helps in reducing overfitting and improving model interpretability.”

3. Describe a time when you had to explain a complex technical concept to a non-technical audience.

This question tests your communication skills and ability to bridge the gap between technical and non-technical stakeholders.

How to Answer

Provide a specific example where you successfully communicated a complex idea, focusing on how you simplified the concept and ensured understanding.

Example

“I once presented a predictive model to our marketing team. I used visual aids to illustrate how the model worked and focused on the business implications rather than the technical details. This helped them understand how to leverage the model for their campaigns.”

4. What methods do you use to validate your machine learning models?

This question assesses your knowledge of model evaluation techniques.

How to Answer

Discuss the validation techniques you employ, such as cross-validation, and the metrics you use to assess model performance.

Example

“I typically use k-fold cross-validation to ensure that my model generalizes well to unseen data. I also monitor metrics like precision, recall, and F1-score to evaluate performance, depending on the problem at hand.”

5. How would you handle missing data in a dataset?

This question evaluates your data preprocessing skills and understanding of data integrity.

How to Answer

Explain the strategies you use to handle missing data, including imputation methods or the decision to remove certain records.

Example

“I assess the extent of missing data first. If it’s minimal, I might use mean or median imputation. For larger gaps, I consider removing those records or using predictive modeling to estimate the missing values.”

Statistics & Probability

1. Explain the difference between Type I and Type II errors.

This question tests your understanding of statistical hypothesis testing.

How to Answer

Clearly define both types of errors and provide context on their implications in decision-making.

Example

“A Type I error occurs when we reject a true null hypothesis, while a Type II error happens when we fail to reject a false null hypothesis. Understanding these errors is crucial in determining the reliability of our statistical conclusions.”

2. How do you determine if a dataset is normally distributed?

This question assesses your knowledge of statistical distributions.

How to Answer

Discuss the methods you use to check for normality, such as visual inspections or statistical tests.

Example

“I use visual methods like Q-Q plots and histograms to assess normality. Additionally, I apply the Shapiro-Wilk test to statistically confirm whether the data deviates from a normal distribution.”

3. Can you explain the concept of p-value and its significance?

This question evaluates your grasp of statistical significance.

How to Answer

Define p-value and explain its role in hypothesis testing.

Example

“The p-value indicates the probability of observing the data, or something more extreme, if the null hypothesis is true. A low p-value suggests that we can reject the null hypothesis, indicating statistical significance.”

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

This question tests your understanding of fundamental statistical principles.

How to Answer

Explain the theorem and its implications for sampling distributions.

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 for making inferences about population parameters.”

5. How would you assess the correlation between two variables?

This question evaluates your ability to analyze relationships in data.

How to Answer

Discuss the methods you would use to measure correlation and interpret the results.

Example

“I would calculate the Pearson correlation coefficient to quantify the linear relationship between the two variables. A value close to 1 or -1 indicates a strong correlation, while a value near 0 suggests no correlation.”

Algorithms

1. Can you explain a sorting algorithm and its time complexity?

This question tests your understanding of algorithms and their efficiency.

How to Answer

Choose a sorting algorithm, explain how it works, and discuss its time complexity in different scenarios.

Example

“I can explain the quicksort algorithm, which uses a divide-and-conquer approach. Its average time complexity is O(n log n), but in the worst case, it can degrade to O(n^2) if the pivot selection is poor.”

2. Describe a situation where you had to optimize an algorithm.

This question assesses your problem-solving skills and ability to improve efficiency.

How to Answer

Provide a specific example of an algorithm you optimized, detailing the initial performance and the improvements made.

Example

“I worked on optimizing a search algorithm that initially had a time complexity of O(n). By implementing a binary search approach, I reduced it to O(log n), significantly improving performance for large datasets.”

3. What is the difference between supervised and unsupervised learning?

This question evaluates your understanding of machine learning paradigms.

How to Answer

Clearly define both types of learning and provide examples of each.

Example

“Supervised learning involves training a model on labeled data, such as predicting house prices based on features. In contrast, unsupervised learning deals with unlabeled data, like clustering customers based on purchasing behavior.”

4. How do you ensure your algorithms are scalable?

This question tests your ability to design algorithms that can handle increasing data sizes.

How to Answer

Discuss the strategies you use to ensure scalability, such as algorithmic efficiency and data partitioning.

Example

“I focus on choosing algorithms with efficient time complexities and consider data partitioning techniques like sharding to distribute workloads across multiple servers, ensuring that performance remains optimal as data grows.”

5. Can you explain the concept of overfitting and how to prevent it?

This question assesses your understanding of model performance and generalization.

How to Answer

Define overfitting and discuss techniques to mitigate it.

Example

“Overfitting occurs when a model learns noise in the training data rather than the underlying pattern, leading to poor performance on unseen data. To prevent it, I use techniques like cross-validation, regularization, and pruning in decision trees.”