Research Foundation of the City University of New York Data Scientist Interview Questions + Guide in 2025

The Research Foundation of the City University of New York (RFCUNY) is dedicated to supporting the educational and research endeavors of CUNY faculty and staff through effective administration of funded programs.

In the role of Data Scientist, you will be pivotal in utilizing data-driven methodologies to inform criminal justice policies in New York City. Your key responsibilities will include designing and implementing analytical structures, developing insightful reports to address critical policy questions, and integrating complex datasets from various sources, including criminal justice agencies and nonprofit service providers. Strong skills in statistical analysis, SQL, and programming languages like R and Python will be essential to your success. The ideal candidate will possess not only technical expertise but also a genuine interest in the intersection of data science and social justice, ensuring that the findings are communicated effectively to stakeholders and policymakers.

This guide will help you prepare effectively for your interview by providing insights into the expectations of the role and the skills necessary to excel within the organization.

The interview process for a Data Scientist position at the Research Foundation of the City University of New York is structured to assess both technical skills and cultural fit within the organization. The process typically unfolds in several key stages:

1. Initial Screening

The first step is an initial screening, which usually takes place via a brief phone or video call. During this stage, a recruiter will discuss your background, experience, and interest in the role. This conversation is also an opportunity for you to learn more about the organization and its mission, particularly in relation to criminal justice research and data analysis.

2. Technical Interview

Following the initial screening, candidates typically participate in a technical interview conducted over Zoom. This interview may involve multiple interviewers and focuses on your proficiency in statistical analysis, coding skills, and familiarity with data science tools such as SQL, R, and Python. Expect to answer questions related to your previous projects, data manipulation techniques, and problem-solving approaches in data science.

3. Panel Interview

Candidates who successfully pass the technical interview are often invited to a panel interview. This session usually involves a group of staff members from the organization, including those directly involved in the research projects. The panel will ask questions about your experience, your understanding of criminal justice issues, and how you would approach specific data-related challenges. This interview typically lasts around 45 minutes to an hour.

4. Practical Assessment

In some cases, candidates may be required to complete a practical assessment or a coding exercise. This could involve analyzing a dataset, creating visualizations, or developing a report that addresses a specific research question relevant to the organization’s work. This step is designed to evaluate your technical skills in a real-world context and your ability to communicate findings effectively.

5. Final Interview

The final stage may include a follow-up interview with key stakeholders or potential supervisors. This conversation often focuses on your fit within the team, your long-term career goals, and how you can contribute to the organization’s mission. It’s also a chance for you to ask any remaining questions about the role and the work environment.

As you prepare for your interview, consider the specific skills and experiences that align with the responsibilities of the Data Scientist role, particularly in relation to statistical analysis and data-driven policy development.

Next, let’s delve into the types of questions you might encounter during the interview process.

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

Understand the Mission and Values

Familiarize yourself with the Research Foundation of the City University of New York (RFCUNY) and its commitment to advancing justice and equity through data-driven policy analysis. Understanding the specific goals of the Data Collaborative for Justice (DCJ) and the Mayor's Office of Criminal Justice (MOCJ) will allow you to align your responses with their mission. Be prepared to discuss how your background and interests in criminal justice can contribute to their objectives.

Prepare for a Collaborative Interview Format

The interview process may involve multiple interviewers and breakout sessions, often conducted via Zoom. Practice articulating your thoughts clearly and concisely, as you may be asked to respond to questions from different team members in a limited timeframe. Be ready to engage in discussions about your previous experiences and how they relate to the role, as well as to demonstrate your ability to work collaboratively in a team setting.

Highlight Your Technical Proficiency

Given the emphasis on data analysis and coding, ensure you can discuss your experience with SQL, R, and Python confidently. Be prepared to provide examples of how you've used these tools in past projects, particularly in relation to statistical analysis and data mining. Familiarize yourself with the types of data sources you might encounter in this role, especially those related to criminal justice, and be ready to discuss how you would approach integrating and analyzing such data.

Showcase Your Problem-Solving Skills

Expect questions that assess your analytical thinking and problem-solving abilities. Prepare to discuss specific challenges you've faced in previous roles and how you overcame them, particularly in data-related contexts. Highlight your ability to balance competing priorities and work independently in fast-paced environments, as these are crucial skills for the position.

Communicate Your Interest in Criminal Justice

Demonstrating a genuine interest in criminal justice and related social issues will set you apart. Be prepared to discuss any relevant experiences or projects that showcase your commitment to this field. If you have familiarity with New York City-specific criminal justice issues, make sure to mention this, as it can strengthen your candidacy.

Prepare for Behavioral Questions

Expect behavioral interview questions that explore how you handle various situations, particularly in collaborative settings. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you provide clear examples that illustrate your skills and experiences. This will help you convey your thought process and decision-making abilities effectively.

Be Ready for Technical Assessments

You may encounter technical assessments or case studies during the interview process. Brush up on your analytical skills and be prepared to demonstrate your ability to design and implement analytical structures or reporting products. Familiarize yourself with common data visualization tools, as well as any advanced data science methods that may be relevant to the role.

Follow Up Thoughtfully

After the interview, consider sending a thoughtful follow-up email to express your appreciation for the opportunity to interview. Use this as a chance to reiterate your enthusiasm for the role and the organization, and to briefly mention any key points from the interview that you found particularly engaging or relevant.

By following these tips and preparing thoroughly, you'll position yourself as a strong candidate for the Data Scientist role at the Research Foundation of the City University of New York. Good luck!

In this section, we’ll review the various interview questions that might be asked during an interview for a Data Scientist position at the Research Foundation of the City University of New York. The interview process will likely focus on your technical skills, experience with data analysis, and understanding of criminal justice issues. Be prepared to discuss your previous work, your analytical approach, and how you can contribute to the organization's mission.

Technical Skills

1. Can you explain your experience with SQL and how you have used it in past projects?

This question assesses your technical proficiency with SQL, which is crucial for the role.

How to Answer

Discuss specific projects where you utilized SQL for data extraction, manipulation, or analysis. Highlight any complex queries you wrote and the impact of your work.

Example

“In my previous role, I used SQL to extract and analyze data from a relational database to identify trends in criminal justice outcomes. I wrote complex queries that joined multiple tables, which allowed us to uncover insights that informed policy recommendations.”

2. Describe a project where you used R or Python for data analysis. What was your approach?

This question evaluates your programming skills and your ability to apply them in real-world scenarios.

How to Answer

Detail the project, the data you worked with, and the specific libraries or techniques you employed. Emphasize the results and how they contributed to the project goals.

Example

“I worked on a project analyzing recidivism rates using Python. I utilized libraries like Pandas for data manipulation and Matplotlib for visualization. My analysis revealed key factors influencing recidivism, which helped shape our intervention strategies.”

3. How do you ensure the accuracy and integrity of your data?

This question probes your understanding of data quality, which is essential for reliable analysis.

How to Answer

Discuss your methods for data validation, cleaning, and verification. Mention any tools or techniques you use to maintain data integrity.

Example

“I implement a rigorous data cleaning process that includes checking for duplicates, handling missing values, and validating data against known benchmarks. I also use automated scripts to regularly audit the data for inconsistencies.”

4. Can you describe your experience with data visualization tools?

This question assesses your ability to communicate data insights effectively.

How to Answer

Mention specific tools you have used, such as Tableau or Matplotlib, and provide examples of how you created visualizations that informed decision-making.

Example

“I have extensive experience with Tableau, where I created interactive dashboards that visualized crime trends over time. These visualizations were instrumental in presenting findings to stakeholders and guiding policy discussions.”

5. What is your experience with cloud environments, and how have you utilized them in your work?

This question evaluates your familiarity with cloud computing, which is increasingly important in data science.

How to Answer

Discuss any cloud platforms you have worked with, such as AWS or Azure, and how you used them for data storage, processing, or analysis.

Example

“I have worked with AWS to set up data pipelines for processing large datasets. By leveraging services like S3 for storage and Lambda for serverless computing, I was able to streamline our data processing workflows significantly.”

Criminal Justice Knowledge

1. Why are you interested in working in the field of criminal justice?

This question gauges your motivation and alignment with the organization's mission.

How to Answer

Share your passion for criminal justice and any relevant experiences that have shaped your interest in this field.

Example

“I am passionate about using data to drive social change, particularly in the criminal justice system. My previous work with community organizations has shown me the impact that informed policy can have on reducing recidivism and promoting fairness.”

2. How do you approach analyzing sensitive data, particularly in the context of criminal justice?

This question assesses your understanding of ethical considerations in data analysis.

How to Answer

Discuss your awareness of privacy concerns and the measures you take to protect sensitive information.

Example

“I prioritize data privacy by anonymizing sensitive information and adhering to ethical guidelines for data use. I also ensure that my analyses comply with legal standards and institutional policies regarding data handling.”

3. Can you provide an example of how data analysis has influenced a policy decision in criminal justice?

This question evaluates your understanding of the practical applications of data analysis in policy-making.

How to Answer

Share a specific case where data analysis led to a significant policy change or decision.

Example

“In a previous project, my analysis of arrest data revealed racial disparities in enforcement practices. This prompted a review of policing strategies and ultimately led to the implementation of bias training for officers.”

4. What are some key metrics you believe should be tracked in criminal justice research?

This question assesses your knowledge of relevant metrics and their importance in policy analysis.

How to Answer

Discuss specific metrics that are critical for understanding criminal justice outcomes and their implications for policy.

Example

“I believe metrics such as recidivism rates, arrest rates by demographic, and case processing times are essential. Tracking these metrics can help identify disparities and inform targeted interventions.”

5. How do you stay updated on current trends and issues in criminal justice?

This question evaluates your commitment to continuous learning in the field.

How to Answer

Mention specific resources, publications, or organizations you follow to stay informed about criminal justice issues.

Example

“I regularly read publications like the Vera Institute of Justice and follow organizations such as the Brennan Center for Justice. I also participate in webinars and conferences to engage with experts in the field.”