Numerator Data Scientist Interview Questions + Guide in 2025

Numerator is a leading data and technology company that provides insights and analytics to empower brands and retailers to make informed decisions based on consumer behavior.

As a Data Scientist at Numerator, you will play a pivotal role in analyzing complex datasets to derive actionable insights that drive business strategies. Your key responsibilities will include designing and implementing data models, developing algorithms, and utilizing statistical methods to interpret data trends while ensuring that solutions are scalable and efficient. You will be expected to have a strong proficiency in SQL and Python, as well as experience with cloud computing platforms, particularly AWS. Your expertise in machine learning and data manipulation using libraries such as Pandas will be crucial for executing projects effectively.

In addition to technical skills, strong problem-solving abilities and interpersonal skills are essential, as you will collaborate with cross-functional teams to communicate findings and provide recommendations. A data-driven mindset and the ability to translate complex data into understandable insights will set you apart in this role.

This guide aims to equip you with the knowledge and confidence to excel during your interview by emphasizing the key skills and traits Numerator values in its Data Scientists.

The interview process for a Data Scientist role at Numerator is structured to assess both technical expertise and interpersonal skills, ensuring candidates are well-rounded and fit for the company's collaborative environment. The process typically unfolds in several key stages:

1. Initial Screening

The initial screening is conducted via a phone call with a recruiter. This conversation lasts about 30 minutes and serves to introduce the candidate to Numerator's culture and values. The recruiter will inquire about your background, skills, and motivations for applying, while also gauging your fit within the company.

2. Technical Interview

Following the initial screening, candidates will participate in a technical interview, which may be conducted via video conferencing. This session typically involves two interviewers: one focusing on technical skills and the other on behavioral aspects. The technical interviewer will assess your proficiency in SQL, cloud computing, coding (particularly in Python), and data manipulation using libraries like Pandas. Expect to solve problems related to SQL queries, cloud service utilization, and algorithmic challenges.

3. Behavioral Interview

The behavioral interview is designed to evaluate your interpersonal skills and how you approach teamwork and problem-solving. Questions may revolve around your past experiences, challenges faced in previous roles, and how you’ve collaborated with others. This stage is crucial for understanding how you align with Numerator's values and work culture.

4. Final Interview

The final interview may involve a panel of interviewers, including senior data scientists and team leads. This round will delve deeper into your technical knowledge, particularly in statistics, algorithms, and machine learning concepts. You may also be asked to present a past project or case study, demonstrating your analytical thinking and problem-solving abilities.

Throughout the interview process, candidates should be prepared to discuss their experiences with data analysis, statistical methods, and any relevant projects that showcase their skills.

Now, let's explore the specific interview questions that candidates have encountered during this process.

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

Understand the Technical Landscape

Familiarize yourself with the technical skills that are crucial for a Data Scientist role at Numerator. Focus on SQL, Python, and data manipulation libraries like Pandas. Be prepared to demonstrate your understanding of cloud computing, particularly AWS services, as well as your ability to write efficient code. Brush up on your knowledge of algorithms and data structures, as you may be asked to solve problems that test your coding skills and algorithmic thinking.

Prepare for SQL and Data Manipulation Questions

SQL proficiency is essential, so practice writing queries that involve complex joins, aggregations, and optimizations. Be ready to explain your thought process when tackling SQL problems, such as finding the second highest salary or optimizing slow-running queries. Additionally, ensure you can discuss how you would handle missing data and perform data aggregation using Pandas, as these are common topics in interviews.

Showcase Your Cloud Computing Knowledge

Since cloud computing is a significant aspect of the role, be prepared to discuss your experience with AWS services. Understand the differences between services like EC2 and Lambda, and be ready to explain how you have utilized these services in your projects. Highlight any strategies you have implemented for managing cloud resources efficiently, as this demonstrates your ability to think critically about cost management.

Highlight Your Problem-Solving Skills

During the interview, you may be asked to describe challenging problems you have encountered in previous projects or internships. Use the STAR (Situation, Task, Action, Result) method to structure your responses, focusing on the specific actions you took and the outcomes achieved. This will not only showcase your technical skills but also your ability to navigate complex situations effectively.

Emphasize Interpersonal Skills

Numerator values interpersonal skills, so be prepared for behavioral questions that assess your teamwork and communication abilities. Reflect on past experiences where you collaborated with others, resolved conflicts, or contributed to a team project. Demonstrating your ability to work well with others will help you align with the company culture and values.

Practice Coding Challenges

Expect to solve coding challenges during the interview, particularly in Python. Practice common algorithmic problems, such as reversing a linked list or implementing data structures using Python. Familiarize yourself with the nuances of algorithmic complexity, as you may be asked to analyze the efficiency of your solutions.

Be Yourself and Engage

Finally, remember to be personable and engage with your interviewers. Show enthusiasm for the role and the company, and don’t hesitate to ask insightful questions about Numerator’s projects, team dynamics, or future goals. This will not only demonstrate your interest but also help you assess if the company is the right fit for you.

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

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Numerator. The interview process will assess a combination of technical skills, including SQL, cloud computing, coding, and data manipulation with Python and Pandas, as well as behavioral competencies to evaluate your interpersonal skills and experiences.

Technical Skills

1. Write a SQL query to find the second highest salary from a table of employees.

This question tests your SQL proficiency and ability to manipulate data effectively.

How to Answer

Explain your thought process while writing the query, focusing on the use of subqueries or window functions to achieve the desired result.

Example

“I would use a subquery to first select the maximum salary and then filter the results to find the second highest. The query would look something like: SELECT MAX(salary) FROM employees WHERE salary < (SELECT MAX(salary) FROM employees);”

2. How would you optimize a slow-running query?

This question assesses your understanding of query performance and optimization techniques.

How to Answer

Discuss various strategies such as indexing, query rewriting, and analyzing execution plans to improve performance.

Example

“To optimize a slow-running query, I would first analyze the execution plan to identify bottlenecks. Then, I would consider adding indexes on frequently queried columns and rewriting the query to reduce complexity, ensuring it only retrieves necessary data.”

3. Explain the difference between INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.

This question evaluates your knowledge of SQL joins and how they affect data retrieval.

How to Answer

Clearly define each type of join and provide examples of when to use them.

Example

“An INNER JOIN returns records that have matching values in both tables, while a LEFT JOIN returns all records from the left table and matched records from the right. A RIGHT JOIN does the opposite, and a FULL JOIN returns all records when there is a match in either left or right table.”

Cloud Computing

4. What are some common AWS services you’ve used, and how did you utilize them in your projects?

This question gauges your familiarity with cloud services and their practical applications.

How to Answer

Mention specific AWS services and describe how you integrated them into your projects to solve problems or enhance functionality.

Example

“I have used AWS S3 for data storage and AWS Lambda for serverless computing. In one project, I utilized S3 to store large datasets and triggered Lambda functions to process data in real-time as it was uploaded.”

5. Explain the differences between AWS EC2 and AWS Lambda.

This question tests your understanding of cloud computing concepts and service offerings.

How to Answer

Discuss the key differences in terms of use cases, pricing models, and operational management.

Example

“AWS EC2 provides virtual servers for running applications, allowing for full control over the environment, while AWS Lambda is a serverless computing service that runs code in response to events without managing servers. EC2 is ideal for long-running applications, whereas Lambda is suited for event-driven architectures.”

Coding and Algorithms

6. Write a Python function to reverse a linked list.

This question assesses your coding skills and understanding of data structures.

How to Answer

Outline your approach to solving the problem and write a clear, efficient function.

Example

“I would define a function that iterates through the linked list, reversing the pointers as I go. The function would look like this: def reverse_linked_list(head): ... and I would ensure to handle edge cases like an empty list.”

7. How would you implement a stack using queues in Python?

This question evaluates your problem-solving skills and understanding of data structures.

How to Answer

Explain the logic behind using two queues to simulate stack behavior and provide a brief code example.

Example

“I would use two queues, where I push elements into the first queue and pop from the second. When popping, I would transfer all but the last element from the first queue to the second, then return the last element.”

Data Manipulation with Pandas

8. How would you handle missing data in a DataFrame using Pandas?

This question tests your data cleaning skills and familiarity with Pandas.

How to Answer

Discuss various methods for handling missing data, such as imputation or removal, and when to use each.

Example

“I would assess the extent of missing data and decide whether to fill it using methods like mean or median imputation or to drop rows or columns with excessive missing values, depending on the analysis requirements.”

9. Explain how to merge two DataFrames on a common key.

This question evaluates your understanding of data manipulation in Pandas.

How to Answer

Describe the merge function and how to specify the key for merging.

Example

“I would use the pd.merge() function, specifying the DataFrames and the common key. For example, merged_df = pd.merge(df1, df2, on='key_column') would combine the two DataFrames based on the specified key.”

10. Describe how you would perform data aggregation and summarization with Pandas.

This question assesses your ability to analyze and summarize data effectively.

How to Answer

Explain the use of functions like groupby() and agg() to perform aggregation.

Example

“I would use df.groupby('column_name').agg({'another_column': 'sum'}) to aggregate data based on a specific column, allowing me to summarize the dataset effectively.”