Institutional Shareholder Services Data Scientist Interview Questions + Guide in 2025

Institutional Shareholder Services (ISS) is a leading provider of corporate governance and responsible investment solutions, helping clients navigate complex investment landscapes.

As a Data Scientist at ISS, you will play a pivotal role in analyzing large datasets to derive actionable insights that inform corporate governance practices and investment strategies. Your key responsibilities will involve developing predictive models, utilizing machine learning techniques, and performing statistical analyses to support various business functions. You will work closely with cross-functional teams to ensure the integration of data-driven insights into decision-making processes.

To excel in this role, you should possess a strong background in programming languages such as Python and Java, along with a solid understanding of database management systems (DBMS) and data preprocessing techniques. Familiarity with object-oriented programming concepts and frameworks like Spring Boot will be beneficial. Additionally, your experience with statistical analysis and machine learning algorithms will be critical for building robust models.

A great fit for ISS will exhibit strong analytical skills, attention to detail, and the ability to communicate complex findings effectively. Given ISS's focus on corporate governance and investment research, an understanding of these domains will enhance your contributions. Collaboration and adaptability are essential traits, as you will be engaging with various stakeholders and adapting to the fast-paced nature of the industry.

This guide aims to equip you with insights into the role and help you prepare effectively for your interview at ISS, ensuring you can showcase your skills and fit for the team.

The interview process for a Data Scientist role at Institutional Shareholder Services (ISS) is structured yet accommodating, designed to assess both technical skills and cultural fit. The process typically unfolds as follows:

1. Online Assessment

The first step in the interview process is an online assessment that tests your technical skills and aptitude. This assessment usually lasts around three hours and covers various topics relevant to the role, including programming languages like Java and Python, as well as data structures and algorithms. Candidates are advised to prepare for logical reasoning and basic quantitative questions, as these are often included in the assessment.

2. Initial HR Interview

Upon successfully passing the online assessment, candidates are invited for an initial interview with an HR representative. This interview is generally conversational and focuses on confirming the details provided in your resume, discussing your background, and understanding your motivations for applying to ISS. Expect to answer questions about your experiences, skills, and how they relate to the Data Scientist position.

3. Technical Interview Rounds

Following the HR interview, candidates typically undergo two technical interview rounds. The first technical interview is often conducted by team members or managers and focuses on your technical expertise, including programming concepts, data analysis techniques, and relevant projects you have worked on. The second technical interview may involve a panel of senior team members who will delve deeper into your technical knowledge and problem-solving abilities. Be prepared to discuss specific technologies and methodologies you have used in your previous work.

4. Final Interview with Hiring Manager

The final stage of the interview process is a one-on-one interview with the hiring manager. This interview assesses your fit within the team and the company culture. It may include situational questions that evaluate how you handle challenges and work collaboratively. The hiring manager will also discuss your long-term career goals and how they align with the objectives of ISS.

Throughout the interview process, candidates are encouraged to ask questions about the company and the role to demonstrate their interest and engagement.

Now that you have an understanding of the interview process, let’s explore the types of questions you might encounter during your interviews.

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

Embrace the Conversational Style

The interview process at Institutional Shareholder Services (ISS) tends to be relaxed and conversational. Approach the interview as a dialogue rather than a formal interrogation. Be yourself, and don’t hesitate to share your thoughts and experiences openly. This will help you connect with the interviewers and showcase your personality, which is valued in their culture.

Prepare for Technical and Situational Questions

While technical skills are essential, be ready to discuss your personal projects and how they relate to the role. Expect questions on programming languages like Java and Python, as well as database management concepts. Additionally, situational questions will likely arise, so think of examples from your past experiences that demonstrate your problem-solving abilities and how you handle challenges in a team setting.

Know Your Resume Inside and Out

The interviewers will focus on your resume, so be prepared to discuss every detail. Highlight your relevant experiences, skills, and projects that align with the role of a Data Scientist. Be concise and direct in your responses to avoid rambling, as clarity is appreciated in their communication style.

Understand the Company and Its Values

Research ISS thoroughly, including its mission, values, and recent developments in the corporate governance space. This knowledge will not only help you answer questions about why you want to work there but also allow you to ask insightful questions that demonstrate your genuine interest in the company.

Be Ready for a Multi-Round Process

The interview process typically involves multiple rounds, including technical assessments and interviews with HR and management. Stay organized and be prepared for each stage. If you encounter a challenging interviewer, maintain your composure and professionalism; remember that the overall experience is what counts.

Communicate Clearly and Confidently

Since interviews are conducted strictly in English, ensure that you articulate your thoughts clearly. Practice your responses to common questions and focus on your communication skills. This will not only help you convey your qualifications effectively but also demonstrate your ability to communicate in a professional environment.

Stay Positive and Open-Minded

While some candidates have reported mixed experiences with interviewers, maintaining a positive attitude can go a long way. If you encounter a difficult interviewer, try to remain calm and focus on showcasing your skills and experiences. Remember, the goal is to find a mutual fit, so be open to feedback and discussions.

Prepare for the Culture Fit

ISS values a supportive and community-oriented work environment. Be ready to discuss how you can contribute to this culture. Share examples of teamwork, collaboration, and how you handle conflicts in a professional setting. This will help you align your values with those of the company.

By following these tips, you can approach your interview with confidence and a clear strategy, increasing your chances of success at Institutional Shareholder Services. Good luck!

Technical Skills

1. Explain the key principles of Object-Oriented Programming (OOP) and how they differ between Java and Python.

Understanding OOP is crucial for a Data Scientist role, especially when working with data structures and algorithms.

How to Answer

Discuss the four main principles: encapsulation, inheritance, polymorphism, and abstraction. Highlight the differences in implementation between Java and Python, focusing on flexibility versus strictness.

Example

“Object-Oriented Programming is centered around four key principles: encapsulation, inheritance, polymorphism, and abstraction. In Java, these principles are strictly enforced, while Python offers more flexibility, allowing for dynamic typing and easier prototyping. For instance, while both languages support inheritance, Python allows multiple inheritance, which can simplify certain designs.”

2. What is normalization in databases, and why is it important?

Normalization is a fundamental concept in database management that ensures data integrity.

How to Answer

Explain the process of organizing data to reduce redundancy and improve data integrity. Mention the different normal forms and their purposes.

Example

“Normalization is the process of organizing data in a database to minimize redundancy and dependency. It involves dividing a database into tables and defining relationships between them. This is crucial for maintaining data integrity and ensuring that updates, deletions, and insertions do not lead to inconsistencies.”

3. Can you explain the concept of joins in SQL?

Joins are essential for querying data from multiple tables in a relational database.

How to Answer

Discuss the different types of joins (INNER, LEFT, RIGHT, FULL) and provide examples of when to use each.

Example

“Joins in SQL allow us to combine rows from two or more tables based on a related column. For instance, an INNER JOIN returns only the rows with matching values in both tables, while a LEFT JOIN returns all rows from the left table and the matched rows from the right table. This is particularly useful for retrieving related data from different sources.”

4. Describe your experience with data preprocessing and why it is important.

Data preprocessing is a critical step in any data analysis or machine learning project.

How to Answer

Highlight the steps involved in data preprocessing, such as cleaning, transforming, and normalizing data, and explain their significance.

Example

“Data preprocessing is vital as it prepares raw data for analysis. This includes cleaning the data to remove inconsistencies, transforming it to a suitable format, and normalizing it to ensure that different scales do not skew the results. For example, I often use techniques like handling missing values and scaling features to improve model performance.”

Situational and Behavioral Questions

1. Describe a time when you had to work under pressure to meet a tight deadline.

This question assesses your ability to handle stress and prioritize tasks effectively.

How to Answer

Provide a specific example, focusing on the actions you took to manage your time and the outcome of the situation.

Example

“In my previous role, I was tasked with delivering a data analysis report within a week. I prioritized my tasks by breaking down the project into smaller milestones and set daily goals. By maintaining open communication with my team and focusing on the most critical aspects, I successfully delivered the report on time, which was well-received by management.”

2. Tell me about a time when you disagreed with a coworker. How did you handle it?

This question evaluates your conflict resolution and communication skills.

How to Answer

Discuss the situation, your perspective, and how you approached the disagreement constructively.

Example

“I once disagreed with a coworker about the approach to a data analysis project. Instead of escalating the issue, I suggested we sit down and discuss our viewpoints. We ended up combining our ideas, which led to a more robust analysis and a successful project outcome. This experience taught me the value of collaboration and open dialogue.”

3. How do you deal with large amounts of data?

This question assesses your data management and analytical skills.

How to Answer

Explain your strategies for handling large datasets, including tools and techniques you use.

Example

“When dealing with large datasets, I typically use data processing tools like Pandas in Python for efficient manipulation. I also employ techniques such as chunking to process data in smaller batches, which helps manage memory usage. Additionally, I ensure that I have a clear understanding of the data structure to optimize my queries and analyses.”

4. What would you do if a coworker doesn’t want to contribute to a team task?

This question evaluates your teamwork and leadership skills.

How to Answer

Discuss your approach to motivating team members and fostering collaboration.

Example

“If a coworker is reluctant to contribute, I would first try to understand their perspective by having a one-on-one conversation. I would express the importance of their input and how it contributes to the team’s success. If necessary, I would involve our manager to ensure that everyone is aligned and motivated to achieve our common goals.”

Company and Role Understanding

1. What do you understand about ISS and its role in corporate governance?

This question assesses your knowledge of the company and its industry.

How to Answer

Provide a brief overview of ISS’s mission and its significance in corporate governance.

Example

“ISS is a leading provider of corporate governance solutions, helping organizations navigate complex governance challenges. Their role is crucial in ensuring transparency and accountability in corporate practices, which ultimately fosters investor confidence and promotes sustainable business practices.”

2. Why do you want to work for ISS?

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

How to Answer

Discuss your interest in the company’s mission and how your skills align with their needs.

Example

“I am drawn to ISS because of its commitment to promoting good corporate governance practices. I believe that my background in data analysis and my passion for ethical business practices align well with ISS’s mission. I am excited about the opportunity to contribute to a company that plays such a vital role in shaping corporate accountability.”

3. How would your coworkers describe you?

This question evaluates your self-awareness and interpersonal skills.

How to Answer

Reflect on feedback you’ve received from colleagues and how it aligns with your professional persona.

Example

“My coworkers would likely describe me as collaborative and detail-oriented. I strive to create a positive team environment by being approachable and supportive, while also ensuring that I pay attention to the finer details in our projects to maintain high-quality outcomes.”

4. What skills do you think are essential for a Data Scientist at ISS?

This question assesses your understanding of the role and its requirements.

How to Answer

Identify key skills relevant to the position and explain their importance.

Example

“I believe that strong analytical skills, proficiency in programming languages like Python and SQL, and a solid understanding of statistical methods are essential for a Data Scientist at ISS. Additionally, effective communication skills are crucial for translating complex data insights into actionable recommendations for stakeholders.”