Bird Data Scientist Interview Questions + Guide in 2025

Bird is an AI-powered Customer Relationship Management (CRM) Platform that simplifies communication for businesses, processing over 5 trillion messages annually across various channels.

As a Data Scientist at Bird, you will be responsible for leveraging data analysis, machine learning, and software development skills to prototype and develop innovative data products that enhance customer interaction and satisfaction. Key responsibilities include collaborating with product management and engineering teams to identify data-driven opportunities, directing the activities of a data science team, and ensuring the integration and evaluation of large, unstructured datasets to extract meaningful insights. A strong foundation in statistics, probability, and algorithms is critical, as is proficiency in programming languages like Python, especially modules relevant to data manipulation and analysis, such as Pandas. A successful candidate will exhibit a deep understanding of machine learning principles, strong analytical skills, and the ability to communicate complex findings effectively to stakeholders.

This guide will help you prepare for your interview by providing insights into the expectations and competencies that Bird seeks, allowing you to showcase your abilities confidently.

The interview process for a Data Scientist role at Bird is structured to assess both technical and interpersonal skills, ensuring candidates are well-suited for the dynamic environment of the company. The process typically unfolds as follows:

1. Initial Screening

The first step is an initial phone screening with a recruiter. This conversation focuses on your background, interest in the role, and alignment with Bird's culture. The recruiter will gauge your enthusiasm for the position and your understanding of the company’s mission, as well as your general qualifications.

2. Technical Interview

Following the initial screening, candidates participate in a technical interview, which is often conducted via video call. This round may involve discussions about your skill set, preferred tools, and relevant experiences. While explicit technical questions may not be the primary focus, candidates should be prepared to demonstrate their analytical capabilities and familiarity with data science concepts.

3. Take-Home Assignment

Candidates are typically given a take-home assignment that requires them to apply their analytical skills to a real-world problem. This assignment often involves data analysis and may include SQL-related tasks or a mini-project that showcases your ability to derive insights from data. The assignment is designed to assess your practical skills and thought process in handling data-driven challenges.

4. Superday Interviews

The final stage of the interview process is a superday, which consists of multiple interviews with various team members, including those from cross-functional teams. This round usually includes technical assessments, such as SQL whiteboarding sessions, where candidates may be asked to solve problems in real-time. Additionally, behavioral questions will be posed to evaluate your fit within the team and your alignment with Bird's values. Each interview typically lasts around 30 minutes, allowing for a comprehensive yet efficient assessment.

As you prepare for your interview, it's essential to familiarize yourself with the types of questions that may arise during this process.

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

Understand the Interview Structure

The interview process at Bird typically begins with a phone screen that focuses on your background and interest in the role, followed by a video conversation with a team member. Familiarize yourself with this structure and prepare to discuss your experiences and how they align with Bird's mission. Expect a take-home assignment that may not be strictly technical but will require strong analytical skills. Be ready to showcase your ability to derive insights from data and present your findings clearly.

Prepare for Technical Assessments

Given the emphasis on SQL and analytics in the role, ensure you are well-versed in SQL queries, particularly aggregate functions and data manipulation techniques. Practice coding challenges on platforms like HackerRank or LeetCode, focusing on SQL problems. Additionally, brush up on your knowledge of statistics and probability, as these are crucial for data analysis. Be prepared for whiteboarding sessions where you may need to demonstrate your thought process and problem-solving skills in real-time.

Showcase Your Analytical Mindset

Bird values candidates who can think critically and derive actionable insights from data. During your interviews, emphasize your analytical capabilities by discussing past projects where you successfully utilized data to inform decisions or solve problems. Be prepared to discuss how you would approach operational questions, such as optimizing product placement or analyzing user behavior, as these types of questions may arise.

Embrace the Company Culture

Bird is known for its dynamic and ambitious culture, where self-starters thrive. Demonstrate your curiosity and willingness to take initiative by sharing examples of how you've tackled challenges in previous roles. Highlight your ability to work independently while also being a collaborative team player. Familiarize yourself with Bird's values and be ready to discuss how you embody them in your work.

Prepare for Behavioral Questions

Expect to discuss your values and how they align with Bird's mission. Prepare for behavioral questions that assess your teamwork, leadership, and problem-solving skills. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you provide clear and concise examples that showcase your fit for the role.

Engage with Your Interviewers

During the superday interviews, you may meet multiple team members, including cross-functional counterparts. Use this opportunity to engage with them by asking insightful questions about their experiences at Bird and the challenges they face. This not only demonstrates your interest in the role but also helps you gauge if the team dynamics align with your working style.

Follow Up Thoughtfully

After your interviews, send a thoughtful follow-up email to express your gratitude for the opportunity to interview and reiterate your enthusiasm for the role. Mention specific points from your conversations that resonated with you, reinforcing your interest in joining the Bird team.

By following these tips and preparing thoroughly, you'll position yourself as a strong candidate for the Data Scientist role at Bird. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Bird. The interview process will likely assess your technical skills in statistics, probability, algorithms, and machine learning, as well as your ability to communicate insights and collaborate with cross-functional teams. Be prepared to demonstrate your analytical thinking and problem-solving capabilities through both technical and behavioral questions.

Statistics and Probability

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

Understanding how to manage missing data is crucial in data analysis, as it can significantly impact the results of your models.

How to Answer

Discuss various techniques such as imputation, deletion, or using algorithms that support missing values. Highlight your reasoning for choosing a specific method based on the context of the data.

Example

“I typically assess the extent and pattern of missing data first. If the missingness is random, I might use mean or median imputation. However, if the missing data is systematic, I would consider using predictive modeling techniques to estimate the missing values, ensuring that the integrity of the dataset is maintained.”

2. Can you explain the Central Limit Theorem and its significance?

The Central Limit Theorem is a fundamental concept in statistics that underpins many statistical methods.

How to Answer

Explain the theorem in simple terms and discuss its implications for sampling distributions and hypothesis testing.

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 original distribution of the data. This is significant because it allows us to make inferences about population parameters even when the population distribution is unknown, as long as we have a sufficiently large sample size.”

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

Understanding errors in hypothesis testing is essential for making informed decisions based on statistical analysis.

How to Answer

Define both types of errors and provide examples to illustrate their implications in a business context.

Example

“A Type I error occurs when we reject a true null hypothesis, essentially a false positive. Conversely, a Type II error happens when we fail to reject a false null hypothesis, leading to a false negative. For instance, in a marketing campaign, a Type I error could mean incorrectly concluding that a campaign is effective when it is not, while a Type II error could mean missing out on a successful campaign.”

4. How would you assess the effectiveness of a marketing campaign using statistical methods?

This question tests your ability to apply statistical analysis to real-world business scenarios.

How to Answer

Discuss metrics you would use, such as conversion rates, and the statistical tests you might apply to evaluate the campaign's performance.

Example

“I would start by defining key performance indicators, such as conversion rates and customer acquisition costs. I would then use A/B testing to compare the campaign's performance against a control group, applying statistical tests like t-tests to determine if the differences observed are statistically significant.”

Machine Learning

1. Describe a machine learning project you have worked on. What was your approach?

This question allows you to showcase your practical experience in machine learning.

How to Answer

Outline the problem, your approach to data collection and preprocessing, the algorithms you used, and the results achieved.

Example

“In a recent project, I developed a predictive model to forecast customer churn. I started by gathering historical customer data and performed feature engineering to identify key predictors. I used a random forest algorithm for its robustness and interpretability, achieving an accuracy of 85% in predicting churn, which helped the marketing team target at-risk customers effectively.”

2. How do you select features for a machine learning model?

Feature selection is critical for building effective models and improving performance.

How to Answer

Discuss techniques such as correlation analysis, recursive feature elimination, or using domain knowledge to select relevant features.

Example

“I typically start with exploratory data analysis to understand the relationships between features and the target variable. I then use techniques like correlation matrices to identify highly correlated features and apply recursive feature elimination to iteratively remove less important features, ultimately selecting those that contribute most to the model’s predictive power.”

3. What is overfitting, and how can it be prevented?

Overfitting is a common issue in machine learning that can lead to poor model performance.

How to Answer

Define overfitting and discuss strategies to mitigate it, such as cross-validation, regularization, or simplifying the model.

Example

“Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern, resulting in poor generalization to new data. To prevent this, I use techniques like cross-validation to ensure the model performs well on unseen data, and I apply regularization methods like Lasso or Ridge to penalize overly complex models.”

4. Explain the difference between supervised and unsupervised learning.

This question tests your foundational knowledge of machine learning paradigms.

How to Answer

Clearly differentiate the two types of learning and provide examples of each.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting house prices based on features like size and location. In contrast, unsupervised learning deals with unlabeled data, where the goal is to identify patterns or groupings, such as clustering customers based on purchasing behavior.”

SQL and Data Manipulation

1. How would you write a SQL query to find the top 10 customers by revenue?

This question assesses your SQL skills and ability to manipulate data.

How to Answer

Outline the SQL functions you would use and the logic behind your query.

Example

“I would use a SELECT statement to retrieve customer IDs and their total revenue, applying the SUM function to aggregate revenue. I would then use the ORDER BY clause to sort the results in descending order and the LIMIT clause to return only the top 10 customers.”

2. Can you explain the difference between INNER JOIN and LEFT JOIN?

Understanding SQL joins is essential for data manipulation and analysis.

How to Answer

Define both types of joins and provide scenarios where each would be appropriate.

Example

“An INNER JOIN returns only the rows that have matching values in both tables, while a LEFT JOIN returns all rows from the left table and the matched rows from the right table, with NULLs for non-matching rows. For instance, if I want to list all customers and their orders, I would use a LEFT JOIN to ensure I include customers who haven’t placed any orders.”

3. How do you optimize a slow-running SQL query?

This question tests your problem-solving skills in database management.

How to Answer

Discuss techniques such as indexing, query restructuring, or analyzing execution plans to improve performance.

Example

“To optimize a slow-running SQL query, I would first analyze the execution plan to identify bottlenecks. I might add indexes to columns frequently used in WHERE clauses or JOIN conditions. Additionally, I would consider restructuring the query to reduce complexity, such as breaking it into smaller subqueries or using temporary tables.”

4. What are aggregate functions in SQL, and can you provide examples?

This question assesses your understanding of SQL functions used for data analysis.

How to Answer

Define aggregate functions and provide examples of how they can be used in queries.

Example

“Aggregate functions perform calculations on a set of values and return a single value. Common examples include COUNT, SUM, AVG, MIN, and MAX. For instance, I might use the SUM function to calculate total sales revenue from a sales table, grouping the results by month to analyze trends over time.”