84.51 Degrees Data Scientist Interview Questions + Guide in 2025

84.51 Degrees is a data and analytics company that leverages data science to create personalized customer experiences and optimize business strategies for their clients.

As a Data Scientist at 84.51 Degrees, you will play a pivotal role in transforming complex data into actionable insights that drive decision-making for grocery retailers and their customers. Key responsibilities include developing statistical models and algorithms, conducting data analysis to understand customer behaviors, and providing data-driven recommendations. You will also be expected to visualize data effectively, communicate findings to non-technical stakeholders, and collaborate with cross-functional teams to solve real-world business problems.

To excel in this role, you should have a strong foundation in statistics and data analysis techniques, proficiency in programming languages such as Python or R, and experience with data visualization tools. A passion for understanding consumer behavior in the grocery sector, along with excellent problem-solving skills and the ability to articulate technical concepts in simple terms, will set you apart as an ideal candidate for the position.

This guide will help you prepare for your interview by equipping you with insights into the expectations and technical knowledge required for a Data Scientist role at 84.51 Degrees.

The interview process for a Data Scientist role at 84.51 Degrees is structured to assess both technical expertise and cultural fit within the company. It typically consists of several rounds, each designed to evaluate different aspects of your qualifications and alignment with the company's values.

1. Initial Phone Interview

The process begins with an initial phone interview, which usually lasts around 30 minutes. This conversation is primarily with a recruiter who will assess your fit for the role and your passion for data science. Expect to discuss your background, interests, and motivations for applying to 84.51 Degrees. This is also an opportunity for you to learn more about the company and its culture.

2. Technical Screen

Following the initial interview, candidates typically undergo a technical screening, which may be conducted via video call. This round often involves two interviewers, one leading the discussion while the other takes notes. You will be asked a variety of technical questions related to data science techniques, such as model selection and data visualization. Be prepared to explain complex concepts in simple terms, as well as to discuss your past projects and problem-solving strategies.

3. Onsite Interviews

Candidates who successfully pass the technical screen are usually invited for onsite interviews, which can consist of multiple rounds. These interviews often include both technical and behavioral components. You may face thought challenges, where you will be given real data problems to solve or asked to analyze datasets. Additionally, expect to engage in discussions about your strengths, weaknesses, and how your experiences align with the company's goals.

4. Final Round

The final round may involve additional interviews with team members or leadership. This stage is crucial for assessing how well you would fit within the team and contribute to ongoing projects. Interviewers will likely explore your understanding of the data science field, your approach to collaboration, and your vision for leveraging data to drive business decisions.

As you prepare for your interviews, consider the types of questions that may arise in each of these rounds, focusing on both your technical skills and your ability to communicate effectively.

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

Understand the Company’s Mission and Values

Before your interview, take the time to familiarize yourself with 84.51 degrees' mission and values. This company is deeply rooted in data-driven decision-making, particularly in the retail and grocery sectors. Understanding their approach to leveraging data to enhance customer experiences will allow you to align your responses with their goals. Be prepared to discuss how your personal values and professional aspirations resonate with their mission.

Prepare for Technical and Conceptual Questions

Expect a mix of technical and conceptual questions during your interview. You may be asked to explain complex data science concepts in simple terms, such as the difference between linear and logistic regression. Practice articulating your thought process clearly and concisely, as if you were explaining it to someone without a technical background. This will demonstrate your ability to communicate effectively, a key skill for a data scientist at 84.51 degrees.

Showcase Problem-Solving Skills

During the interview, you may encounter problem-solving scenarios that require you to think critically and creatively. Be prepared to discuss how you would approach real-world data challenges, particularly those relevant to the grocery and retail industries. Familiarize yourself with common data visualization techniques and be ready to visualize data on the spot, as this is a crucial aspect of the role.

Emphasize Collaboration and Teamwork

84.51 degrees values collaboration and teamwork. Be ready to share examples of how you have successfully worked in teams, particularly in data-driven projects. Highlight your ability to engage with cross-functional teams and how you can contribute to a collaborative environment. This will show that you are not only technically proficient but also a team player who can thrive in their culture.

Prepare for Behavioral Questions

Expect behavioral questions that assess your fit within the company culture. Reflect on your past experiences and be ready to discuss your strengths, weaknesses, and how you handle challenges. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you provide clear and concise examples that demonstrate your skills and adaptability.

Be Ready for a Conversational Interview Style

Interviews at 84.51 degrees tend to be more conversational than strictly formal. Approach the interview as a dialogue rather than a one-sided Q&A. Engage with your interviewers, ask insightful questions about their work, and express genuine interest in the projects they are involved in. This will help you build rapport and leave a positive impression.

Follow Up with Insightful Questions

At the end of your interview, take the opportunity to ask thoughtful questions that reflect your interest in the role and the company. Inquire about the team dynamics, ongoing projects, or how data science is evolving within the organization. This not only shows your enthusiasm but also helps you gauge if the company is the right fit for you.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Data Scientist role at 84.51 degrees. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at 84.51 degrees. The interview process will assess your technical skills, problem-solving abilities, and cultural fit within the company. Be prepared to discuss your experience with data analysis, machine learning techniques, and your approach to data-driven decision-making.

Technical Skills

1. Explain the difference between linear and logistic regression as if you were talking to a 7-year-old.

This question tests your ability to communicate complex concepts in simple terms, which is crucial for collaboration with non-technical stakeholders.

How to Answer

Use relatable analogies and simple language to explain the concepts. Focus on the core differences without getting bogged down in technical jargon.

Example

“Imagine you have a toy car that can go really fast on a straight road. That’s like linear regression, which helps us predict things that can keep going up or down. Now, if you have a toy that can only go forward or backward, like a light switch that can be on or off, that’s like logistic regression, which helps us predict yes or no answers.”

2. Given a problem setup, what model would you use to solve it?

This question assesses your understanding of various data science models and your ability to apply them to real-world problems.

How to Answer

Discuss the problem context, the data available, and the characteristics of different models. Justify your choice based on the problem requirements.

Example

“For a problem predicting customer churn, I would use a logistic regression model because the outcome is binary—whether a customer will leave or stay. This model is effective for binary classification and can provide insights into the factors influencing customer decisions.”

3. How would you visualize a dataset to convey key insights?

This question evaluates your data visualization skills and your ability to communicate findings effectively.

How to Answer

Mention specific visualization tools and techniques you would use, and explain how they help in understanding the data.

Example

“I would use a combination of bar charts and scatter plots to visualize the dataset. Bar charts can effectively show categorical data comparisons, while scatter plots can illustrate relationships between two continuous variables, helping to identify trends and outliers.”

4. How would you predict customer trends in grocery shopping?

This question tests your analytical thinking and understanding of consumer behavior.

How to Answer

Discuss the data sources you would analyze and the methods you would use to derive insights.

Example

“I would analyze historical purchase data, customer demographics, and seasonal trends. Using time series analysis, I could identify patterns and forecast future buying behaviors, which would help in inventory management and marketing strategies.”

5. Describe a past project where you used data to solve a problem.

This question allows you to showcase your practical experience and problem-solving skills.

How to Answer

Outline the project’s objective, your role, the data used, and the outcome. Highlight any challenges faced and how you overcame them.

Example

“In my last project, I analyzed customer feedback data to improve product features. I used sentiment analysis to categorize feedback and identified key areas for improvement. As a result, we implemented changes that led to a 20% increase in customer satisfaction ratings.”

Behavioral Questions

1. What are your strengths and weaknesses as a data scientist?

This question assesses your self-awareness and ability to reflect on your professional development.

How to Answer

Be honest about your strengths and provide examples. For weaknesses, mention areas for improvement and how you are addressing them.

Example

“One of my strengths is my ability to communicate complex data insights to non-technical stakeholders, which I demonstrated in my last role. A weakness I’m working on is my proficiency in deep learning; I’ve been taking online courses to enhance my skills in that area.”

2. Why are you interested in the data scientist position at 84.51 degrees?

This question gauges your motivation and alignment with the company’s mission.

How to Answer

Express your enthusiasm for the role and the company, and connect your skills and interests to their objectives.

Example

“I’m excited about the opportunity at 84.51 degrees because I admire your commitment to leveraging data to enhance customer experiences in the grocery sector. My background in data analysis and passion for consumer behavior align perfectly with your mission.”

3. Tell me about a time you faced a challenge in a project. How did you handle it?

This question evaluates your problem-solving skills and resilience.

How to Answer

Describe the challenge, your approach to resolving it, and the outcome. Focus on what you learned from the experience.

Example

“In a previous project, we faced data quality issues that delayed our analysis. I organized a team meeting to identify the root cause and implemented a data cleaning process. This not only resolved the issue but also improved our workflow for future projects.”

4. How do you stay updated with the latest trends in data science?

This question assesses your commitment to continuous learning and professional development.

How to Answer

Mention specific resources, communities, or courses you engage with to stay informed about industry trends.

Example

“I regularly read industry blogs, participate in online forums, and attend webinars. I also take courses on platforms like Coursera to learn about new tools and techniques, ensuring I stay current in this rapidly evolving field.”

5. How do you approach teamwork in data science projects?

This question evaluates your collaboration skills and ability to work in a team environment.

How to Answer

Discuss your approach to collaboration, communication, and how you value diverse perspectives in a team.

Example

“I believe in fostering open communication and encouraging team members to share their ideas. In my last project, I organized regular check-ins to discuss progress and challenges, which helped us leverage each other’s strengths and ultimately led to a successful outcome.”