Eshares, Inc. Data Scientist Interview Questions + Guide in 2025

Eshares, Inc. is a forward-thinking technology company that specializes in transforming traditional accounting processes into innovative software solutions for equity management and financial growth.

As a Data Scientist at Eshares, you'll play a key role in leveraging data to drive strategic decisions across the organization. Your primary responsibilities will include standardizing and documenting core metrics, generating insights from historical data, and partnering with product managers to assess the impact of new features. You will also be tasked with re-evaluating existing data infrastructure, presenting findings to diverse audiences, and training team members on self-service BI tools.

To excel in this role, you should possess a solid foundation in statistics, probability, and algorithms, alongside proficiency in Python, SQL, and data modeling tools like dbt and Looker. Strong communication skills are essential as you'll work closely with stakeholders to understand business problems and deliver actionable data solutions. Successful candidates will demonstrate a self-starter mentality, an ability to prioritize tasks effectively, and a keen attention to detail.

This guide is designed to help you navigate the interview process by equipping you with insights into the expectations and competencies that Eshares values in a Data Scientist, ultimately enhancing your chances of success.

The interview process for a Data Scientist at Eshares, Inc. is structured to assess both technical skills and cultural fit within the company. It typically consists of several rounds, each designed to evaluate different aspects of your expertise and experience.

1. Initial Recruiter Call

The first step in the interview process is a 30-minute phone call with a recruiter. This conversation is generally friendly and serves as an introduction to the role and the company. The recruiter will ask about your background, data experience, and the technology stack you have worked with. This is also an opportunity for you to ask questions about the company culture and the specifics of the Data Scientist role.

2. Hiring Manager Interview

Following the initial call, candidates will have a 30-minute interview with the hiring manager. This session is more focused and may feel more structured. The hiring manager will inquire about your previous projects, your motivations for applying, and how your experience aligns with the needs of the team. Be prepared to discuss your approach to data science and how you have contributed to past projects.

3. Technical Assessment

Candidates will then complete a technical assessment, which may include a live coding session focused on SQL. This part of the interview is designed to evaluate your technical proficiency and problem-solving skills in real-time. You may also be required to complete a take-home assignment using Python, where you will demonstrate your ability to analyze data and present your findings.

4. Virtual Panel Interview

The final stage of the interview process is a virtual panel interview. This session typically involves multiple team members and focuses on case studies and technical discussions. You will be expected to present your analysis and insights, showcasing your ability to communicate complex data findings to diverse audiences, including product managers and other stakeholders. This round will also assess your collaborative skills and how you approach data-driven decision-making.

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

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

Understand the Interview Structure

Familiarize yourself with the interview process at Carta, which typically includes an initial conversation with a recruiter, followed by a technical interview with the hiring manager, a live coding session focused on SQL, a take-home Python assignment, and a virtual panel discussing product case studies and technical topics. Knowing this structure will help you prepare effectively for each stage.

Showcase Your Technical Proficiency

Given the emphasis on SQL and Python in the role, ensure you are well-versed in these languages. Practice SQL queries that involve complex joins, window functions, and data transformations. For Python, focus on libraries relevant to data science, such as Pandas and NumPy. Be prepared to discuss your experience with dbt, Snowflake, and Looker, as these tools are part of the preferred tech stack.

Prepare for Behavioral Questions

Expect questions that assess your experience in data science and your ability to collaborate with stakeholders. Be ready to discuss specific projects you've worked on, the methodologies you employed, and how your work impacted the business. Highlight your communication skills, as presenting findings to diverse audiences is a key aspect of the role.

Emphasize Your Problem-Solving Skills

Carta values candidates who can turn ambiguous problems into structured analytical projects. Prepare examples that demonstrate your analytical thinking and how you've used data to drive decision-making. Discuss how you approach learning new data sets and your experience in mentoring junior team members, as this reflects your ability to elevate the team's capabilities.

Align with Company Culture

Carta is committed to unlocking equity ownership and values transparency, helpfulness, and leadership. Show your alignment with these values by discussing how you've contributed to a collaborative team environment and how you prioritize stakeholder engagement. Be prepared to articulate your understanding of Carta's mission and how you can contribute to it.

Practice Clear Communication

Since the role involves presenting findings to executives and other stakeholders, practice articulating your thoughts clearly and concisely. Use storytelling techniques to make your data insights relatable and impactful. Tailor your communication style to suit different audiences, ensuring that complex data concepts are easily understood.

Be Ready for Technical Challenges

During the live coding session, stay calm and think aloud as you work through problems. This will help the interviewer understand your thought process. If you encounter a challenge, don't hesitate to ask clarifying questions or discuss alternative approaches. Demonstrating your problem-solving approach is often just as important as arriving at the correct solution.

Follow Up with Questions

At the end of your interviews, have thoughtful questions prepared that reflect your interest in the role and the company. Inquire about the data team's current projects, how success is measured in the role, and opportunities for professional development. This not only shows your enthusiasm but also helps you gauge if the company is the right fit for you.

By following these tips, you can present yourself as a strong candidate who is not only technically proficient but also a great cultural fit for Carta. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Eshares, Inc. Candidates should focus on demonstrating their technical expertise, problem-solving abilities, and communication skills, as these are crucial for the role. The questions will cover a range of topics, including data analysis, statistical methods, and collaboration with stakeholders.

Experience and Background

1. What motivated you to apply for this position at Eshares, Inc.?

Understanding your motivation helps the interviewer gauge your alignment with the company's mission and values.

How to Answer

Discuss your interest in the company's mission and how your skills and experiences align with the role's responsibilities.

Example

“I am passionate about using data to drive business decisions, and Eshares’ mission to unlock equity ownership resonates with me. I believe my experience in data analysis and my commitment to fostering data-driven cultures can contribute significantly to your team.”

Technical Skills

2. Can you describe your experience with SQL and how you use it in your daily work?

SQL is a critical skill for data scientists, and the interviewer wants to understand your proficiency.

How to Answer

Provide specific examples of how you have used SQL to extract, manipulate, and analyze data in previous roles.

Example

“In my previous role, I used SQL daily to query large datasets, create complex joins, and generate reports for stakeholders. For instance, I developed a dashboard that tracked key performance metrics, which helped the team make informed decisions.”

3. How do you approach data modeling and what tools do you prefer?

Data modeling is essential for structuring data for analysis, and the interviewer is interested in your methodology.

How to Answer

Discuss your preferred tools and methodologies for data modeling, and provide examples of how you have applied them.

Example

“I typically use dbt for data modeling because it allows for version control and collaboration. In my last project, I created a data model that streamlined our reporting process, reducing the time spent on data preparation by 30%.”

4. Describe a project where you used Python for data analysis. What libraries did you use?

Python is a key programming language for data scientists, and the interviewer wants to assess your technical skills.

How to Answer

Mention specific libraries you used and how they contributed to the project's success.

Example

“I worked on a project where I used Python with libraries like Pandas and NumPy to analyze customer behavior data. This analysis led to actionable insights that improved our marketing strategy, resulting in a 15% increase in customer engagement.”

5. How do you ensure the quality and accuracy of your data?

Data quality is crucial for reliable analysis, and the interviewer wants to know your approach.

How to Answer

Explain your methods for validating and cleaning data, and provide examples of challenges you’ve faced.

Example

“I implement a rigorous data validation process that includes checking for duplicates, missing values, and outliers. In one instance, I discovered a significant data entry error that, if left unchecked, would have skewed our analysis. I corrected it and established a protocol to prevent similar issues in the future.”

Collaboration and Communication

6. How do you partner with stakeholders to understand their business problems?

Collaboration is key in data science, and the interviewer wants to know how you engage with non-technical teams.

How to Answer

Discuss your approach to communication and how you gather requirements from stakeholders.

Example

“I schedule regular meetings with stakeholders to discuss their needs and challenges. By asking open-ended questions and actively listening, I ensure I fully understand their business problems, which allows me to tailor my analyses to provide the most relevant insights.”

7. Can you give an example of a time you presented your findings to a non-technical audience?

The ability to communicate complex data insights clearly is essential for this role.

How to Answer

Describe the context of the presentation, your approach, and the outcome.

Example

“I presented a data analysis report to our marketing team, focusing on customer segmentation. I used visualizations to simplify the data and highlighted key insights that informed their campaign strategy. The team appreciated the clarity and was able to implement changes that improved our targeting efforts.”

8. What strategies do you use to document your work and ensure knowledge sharing within your team?

Documentation is important for maintaining continuity and collaboration.

How to Answer

Explain your documentation practices and how they benefit your team.

Example

“I maintain detailed documentation of my analyses and models in a shared repository. I also conduct training sessions to help team members understand the tools and methodologies I use, fostering a culture of knowledge sharing and collaboration.”

9. Describe a time when you had to mentor a junior team member. What was your approach?

Mentoring is a valuable skill, and the interviewer wants to know how you support others.

How to Answer

Discuss your mentoring style and provide an example of a successful mentoring experience.

Example

“I mentored a junior analyst by providing regular feedback on their work and encouraging them to take on challenging projects. I also set up weekly check-ins to discuss their progress and address any questions. This approach helped them gain confidence and improve their analytical skills significantly.”

10. How do you stay updated with the latest trends and technologies in data science?

Continuous learning is vital in the fast-evolving field of data science.

How to Answer

Share your strategies for professional development and staying informed.

Example

“I regularly attend data science webinars and conferences, and I’m an active member of several online communities. I also follow industry leaders on social media and read relevant publications to keep up with the latest trends and technologies.”