Ōura Data Scientist Interview Questions + Guide in 2025

Ōura is a rapidly growing health technology company dedicated to empowering individuals to unlock their inner potential through innovative products like the Oura Ring, which provides insights into readiness, activity, and sleep quality.

The Data Scientist role at Ōura is pivotal in leveraging data and artificial intelligence to enhance supply chain analytics and optimize various operational processes. Key responsibilities include analyzing logistics and supplier performance to identify inefficiencies, developing forecasting models to enhance inventory management, and creating robust ETL pipelines for data processing. Ideal candidates will possess strong skills in statistics, algorithms, and machine learning, particularly with a focus on time series data and optimization. A collaborative mindset is crucial as the role involves working closely with cross-functional teams across supply chain, hardware, and finance to drive data-driven decision-making that aligns with Ōura's mission of promoting healthier lifestyles.

This guide will help you prepare for your job interview by providing insights into the key skills and experiences that Ōura values in a Data Scientist, ensuring you can confidently showcase your fit for the role.

The interview process for a Data Scientist role at Ōura is structured to assess both technical and interpersonal skills, ensuring candidates align with the company's mission and values. Here’s what you can expect:

1. Initial Screening

The first step in the interview process is a 30-45 minute phone call with a recruiter. This conversation will focus on your background, experience, and motivation for applying to Ōura. The recruiter will also provide insights into the company culture and the specifics of the Data Scientist role, ensuring that you understand the expectations and responsibilities.

2. Technical Assessment

Following the initial screening, candidates typically undergo a technical assessment, which may be conducted via a coding platform or through a live coding session. This assessment will evaluate your proficiency in Python and SQL, as well as your understanding of statistics, algorithms, and data engineering principles. Expect to solve problems related to data manipulation, statistical analysis, and possibly even machine learning concepts relevant to supply chain analytics.

3. Behavioral Interview

After successfully completing the technical assessment, candidates will participate in a behavioral interview. This round is designed to gauge your soft skills, such as communication, teamwork, and problem-solving abilities. You will be asked to provide examples from your past experiences that demonstrate your ability to work collaboratively with cross-functional teams and manage multiple projects effectively.

4. Onsite or Virtual Interviews

The final stage of the interview process typically consists of multiple rounds of interviews with various team members, including data scientists, product managers, and possibly executives. These interviews can be conducted onsite or virtually, depending on your location. Each interview will last approximately 45 minutes and will cover a mix of technical questions, case studies, and discussions about your previous work and how it relates to Ōura's goals. You may also be asked to present a project or case study that showcases your analytical skills and thought process.

5. Final Interview

In some cases, a final interview may be conducted with senior leadership or a hiring manager. This is an opportunity for you to discuss your vision for the role, how you can contribute to Ōura's mission, and any questions you may have about the company or team dynamics.

As you prepare for these interviews, it’s essential to be ready for a variety of questions that will assess both your technical expertise and your fit within the Ōura culture.

Ōura Data Scientist Interview Questions

In this section, we’ll review the various interview questions that might be asked during an interview for a Data Scientist position at Ōura. The interview will likely focus on your technical skills in data analysis, machine learning, and supply chain analytics, as well as your ability to communicate complex concepts to diverse teams. Be prepared to demonstrate your problem-solving abilities and your understanding of how data can drive business decisions.

Machine Learning

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

Understanding the fundamental concepts of machine learning is crucial for this role, as you will be expected to apply these techniques in real-world scenarios.

How to Answer

Discuss the definitions of both supervised and unsupervised learning, providing examples of each. Highlight the types of problems each method is best suited for.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting sales based on historical data. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns or groupings, like customer segmentation based on purchasing behavior.”

2. Describe a machine learning project you have worked on. What challenges did you face?

This question assesses your practical experience and problem-solving skills in machine learning.

How to Answer

Outline the project, your role, the challenges encountered, and how you overcame them. Focus on the impact of your work.

Example

“I worked on a project to predict customer churn using logistic regression. One challenge was dealing with imbalanced data. I implemented techniques like SMOTE to balance the dataset, which improved our model's accuracy by 15%.”

3. How do you evaluate the performance of a machine learning model?

Evaluating model performance is critical in ensuring the reliability of your predictions.

How to Answer

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

Example

“I evaluate model performance using multiple metrics. For classification tasks, I often look at accuracy and F1 score to balance precision and recall. For regression tasks, I use RMSE and R-squared to assess how well the model fits the data.”

4. What techniques do you use for feature selection?

Feature selection is vital for improving model performance and interpretability.

How to Answer

Mention techniques like recursive feature elimination, LASSO regression, and tree-based methods, and explain their importance.

Example

“I use recursive feature elimination to systematically remove features and assess model performance. Additionally, I apply LASSO regression to penalize less important features, which helps in reducing overfitting and improving model interpretability.”

5. Can you explain how you would approach a time series forecasting problem?

Time series forecasting is a key skill for this role, especially in supply chain analytics.

How to Answer

Outline the steps you would take, including data preparation, model selection, and evaluation.

Example

“I would start by visualizing the data to identify trends and seasonality. Then, I would preprocess the data, possibly using techniques like differencing to stabilize the mean. For modeling, I might use ARIMA or exponential smoothing, and I would evaluate the model using metrics like MAE or RMSE.”

Statistics & Probability

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

Understanding statistical principles is essential for data analysis and interpretation.

How to Answer

Explain the theorem and its implications for sampling distributions.

Example

“The Central Limit Theorem states that the distribution of the sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution. This is crucial because it allows us to make inferences about population parameters even when the population distribution is unknown.”

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

Handling missing data is a common challenge in data science.

How to Answer

Discuss various strategies such as imputation, deletion, or using algorithms that support missing values.

Example

“I handle missing data by first assessing the extent and pattern of the missingness. If it's minimal, I might use mean or median imputation. For larger gaps, I consider using predictive models to estimate missing values or even dropping the feature if it’s not critical.”

3. Explain the concept of p-value in hypothesis testing.

A solid grasp of hypothesis testing is fundamental for data-driven decision-making.

How to Answer

Define p-value and its role in determining statistical significance.

Example

“A p-value indicates the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value (typically < 0.05) suggests that we can reject the null hypothesis, indicating that our findings are statistically significant.”

4. What is the difference between Type I and Type II errors?

Understanding errors in hypothesis testing is crucial for interpreting results.

How to Answer

Define both types of errors and their implications in decision-making.

Example

“A Type I error occurs when we reject a true null hypothesis, leading to a false positive. A Type II error happens when we fail to reject a false null hypothesis, resulting in a false negative. Balancing these errors is essential in hypothesis testing to ensure reliable conclusions.”

5. How would you explain statistical significance to a non-technical stakeholder?

Communicating complex concepts to non-technical audiences is vital in this role.

How to Answer

Use simple language and relatable examples to convey the concept.

Example

“I would explain statistical significance as a way to determine if our findings are likely due to chance. For instance, if we find that a new marketing strategy increases sales, statistical significance helps us understand if this increase is likely real or just a random fluctuation.”