Institutional Shareholder Services Machine Learning Engineer Interview Questions + Guide in 2025

Institutional Shareholder Services (ISS) is a leading provider of corporate governance and responsible investment solutions, helping investors make informed decisions.

As a Machine Learning Engineer at ISS, you will play a pivotal role in developing and implementing machine learning models and algorithms that enhance the firm's analytical capabilities. You will be expected to work collaboratively with data scientists and software engineers to design scalable solutions that provide insights into corporate governance and investment strategies. Key responsibilities include creating predictive models, optimizing existing algorithms, and ensuring the integrity and quality of data used for analysis.

To excel in this position, you should possess strong programming skills in languages such as Python and Java, with practical experience in data structures and algorithms. A solid understanding of machine learning frameworks and libraries (like TensorFlow or PyTorch) is essential, alongside familiarity with database management systems and SQL. Additionally, an analytical mindset coupled with problem-solving abilities and effective communication skills will enable you to convey complex technical concepts to non-technical stakeholders.

This guide will equip you with the necessary knowledge and insights to confidently navigate your interview process, allowing you to showcase your skills and align your experiences with the values of ISS.

The interview process for a Machine Learning Engineer at Institutional Shareholder Services (ISS) is structured yet conversational, designed to assess both technical skills and cultural fit. The process typically unfolds in several stages, allowing candidates to showcase their expertise while also getting a feel for the company environment.

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 concepts in data structures and algorithms. Candidates are encouraged to approach this test with a clear understanding of the technical requirements outlined in the job description.

2. Initial HR Interview

Upon passing the online assessment, candidates are invited for an initial interview with an HR representative. This interview is typically brief, lasting about 30 minutes, and focuses on confirming the details in your resume, discussing your background, and assessing your communication skills. Expect to answer general questions about your experiences and motivations for applying to ISS.

3. Technical Interviews

Following the HR interview, candidates will undergo one or more technical interviews. These interviews may involve discussions with team members or technical leads and can vary in format. Expect questions that delve into your understanding of machine learning concepts, programming skills, and past projects. Interviewers may ask you to explain specific algorithms, discuss your approach to problem-solving, or even solve coding challenges in real-time.

4. Panel Interview

In some cases, candidates may face a panel interview, which includes multiple interviewers from different departments. This stage is designed to evaluate how well you can articulate your thoughts and collaborate with others. Questions may range from technical inquiries to situational and behavioral questions, allowing the panel to gauge your fit within the team and the company culture.

5. Final Interview with Hiring Manager

The final step in the interview process is typically a one-on-one interview with the hiring manager. This interview focuses on your qualifications, career aspirations, and how you can contribute to the team. It’s an opportunity for you to ask more in-depth questions about the role and the company, ensuring that both you and the employer are aligned in expectations.

Throughout the process, candidates are encouraged to be themselves and engage in a two-way conversation, as the interviewers at ISS are known for their friendly and accommodating demeanor.

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 Atmosphere

The interview process at Institutional Shareholder Services (ISS) is known for its relaxed and friendly environment. Approach the interview as a conversation 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 Conceptual Questions

As a Machine Learning Engineer, you should be well-versed in programming languages such as Java and Python, as well as database management concepts. Expect questions that delve into Object-Oriented Programming (OOP) principles, data structures, and algorithms. Be ready to discuss your personal projects and how you applied machine learning techniques to solve real-world problems. This will demonstrate your practical experience and understanding of the field.

Highlight Your Problem-Solving Skills

During the interview, you may encounter situational questions that assess your problem-solving abilities. Prepare to discuss specific instances where you faced challenges in your projects or work experience. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you clearly articulate the context and your contributions.

Be Ready for Behavioral Questions

Expect a mix of technical and behavioral questions. Interviewers will likely ask about your teamwork experiences, how you handle conflict, and your approach to meeting tight deadlines. Reflect on your past experiences and prepare concise, relevant examples that highlight your soft skills and adaptability.

Understand the Company and Its Values

Familiarize yourself with ISS’s mission, values, and the specific role you are applying for. This knowledge will not only help you answer questions about why you want to work there but also allow you to tailor your responses to align with the company’s goals. Demonstrating an understanding of their focus on corporate governance and shareholder services will show your genuine interest in the position.

Communicate Clearly and Confidently

Since interviews are conducted strictly in English, ensure that you communicate your thoughts clearly and confidently. Practice articulating your ideas and technical concepts in a straightforward manner. Avoid rambling; instead, aim for concise and impactful responses that directly address the questions asked.

Prepare for a Multi-Round Process

The interview process typically involves multiple rounds, including technical assessments and interviews with HR and management. Be prepared for a thorough evaluation of your skills and fit for the team. Stay organized and keep track of your interview schedule, as timely responses and follow-ups are appreciated.

Stay Positive and Open-Minded

While some candidates have reported mixed experiences with interviewers, maintaining a positive attitude throughout the process is crucial. If you encounter a challenging interviewer, focus on presenting your best self and showcasing your qualifications. Remember, the goal is to find a mutual fit, so approach each interaction with an open mind.

By following these tips and preparing thoroughly, you can enhance your chances of success in the interview process at ISS. 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 Machine Learning Engineer, as it helps in structuring code efficiently. Be prepared to discuss the principles of inheritance, encapsulation, and polymorphism, and how they manifest in both languages.

How to Answer

Discuss the four main principles of OOP and provide examples of how each principle is implemented in both Java and Python. Highlight the differences in flexibility and strictness between the two languages.

Example

“In Java, encapsulation is enforced through access modifiers, while Python uses naming conventions. For instance, in Java, we can use private and public keywords, whereas in Python, we rely on underscore prefixes. This flexibility in Python allows for more dynamic coding, while Java’s strictness can lead to more robust applications.”

2. What is your experience with data preprocessing in machine learning?

Data preprocessing is a critical step in any machine learning project. Be ready to discuss your methods and tools used for cleaning and preparing data.

How to Answer

Explain the various techniques you have used for data cleaning, normalization, and transformation. Mention any libraries or frameworks you are familiar with, such as Pandas or Scikit-learn.

Example

“I have extensive experience with data preprocessing using Pandas. I typically start by handling missing values through imputation or removal, followed by normalization to ensure that the data is on a similar scale. For instance, in a recent project, I used Min-Max scaling to prepare the data for a neural network model.”

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

SQL joins are fundamental for data manipulation and retrieval. Be prepared to discuss different types of joins and their use cases.

How to Answer

Describe the various types of joins (INNER, LEFT, RIGHT, FULL) and provide examples of when to use each type.

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. For example, if I have a table of customers and a table of orders, an INNER JOIN would show only customers who have placed orders, while a LEFT JOIN would show all customers, including those who haven’t placed any orders.”

4. What are some common machine learning algorithms you have implemented?

Discussing your experience with machine learning algorithms is essential for this role. Be prepared to talk about both supervised and unsupervised learning techniques.

How to Answer

Mention specific algorithms you have worked with, such as linear regression, decision trees, or clustering algorithms. Discuss the context in which you used them and the outcomes.

Example

“I have implemented various algorithms, including decision trees for classification tasks and K-means clustering for customer segmentation. In one project, I used a decision tree to predict customer churn, which helped the marketing team target at-risk customers effectively.”

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 manage time effectively.

How to Answer

Provide a specific example of a project where you faced a tight deadline. Discuss how you prioritized tasks and managed your time.

Example

“During my last project, we had a tight deadline to deliver a machine learning model for a client. I prioritized tasks by breaking down the project into smaller milestones and focused on the most critical components first. This approach allowed us to deliver the model on time, and we received positive feedback from the client.”

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

This question evaluates your conflict resolution skills and ability to work in a team.

How to Answer

Discuss a specific instance where you had a disagreement, how you approached the situation, and what the outcome was.

Example

“I once disagreed with a colleague about the choice of algorithm for a project. I suggested we hold a meeting to discuss our perspectives and review the data together. This collaborative approach led us to a consensus on using a hybrid model that combined both our ideas, ultimately improving the project’s performance.”

3. How do you handle large amounts of data?

This question assesses your data management skills and familiarity with tools and techniques.

How to Answer

Discuss your experience with data storage, processing, and analysis tools. Mention any specific technologies you have used.

Example

“I handle large datasets by utilizing cloud storage solutions like AWS S3 for storage and Apache Spark for processing. In a recent project, I processed terabytes of data using Spark’s distributed computing capabilities, which significantly reduced processing time and allowed for real-time analytics.”

4. What motivates you to work in the field of machine learning?

This question helps interviewers understand your passion and commitment to the field.

How to Answer

Share your personal motivations and interests in machine learning, and how they align with the company’s goals.

Example

“I am motivated by the potential of machine learning to solve complex problems and drive innovation. The ability to derive insights from data and create predictive models excites me, especially in a company like ISS that values data-driven decision-making.”

Company Knowledge

1. What do you know about Institutional Shareholder Services (ISS)?

This question assesses your research and understanding of the company.

How to Answer

Discuss the company’s mission, services, and any recent news or developments that you find relevant.

Example

“I understand that ISS provides governance solutions and advisory services to institutional investors. I admire your commitment to promoting transparency and accountability in corporate governance, which aligns with my values as a data professional.”

2. Why do you want to work for ISS?

This question evaluates your interest in the company and the role.

How to Answer

Explain why you are drawn to the company and how your skills and interests align with its mission.

Example

“I want to work for ISS because I am passionate about using data to drive impactful decisions in corporate governance. I believe my background in machine learning can contribute to your mission of providing valuable insights to investors.”

3. How do you see yourself contributing to ISS in the next five years?

This question assesses your long-term vision and commitment to the company.

How to Answer

Discuss your career goals and how they align with the company’s growth and objectives.

Example

“In the next five years, I see myself taking on more leadership responsibilities within the machine learning team at ISS. I aim to develop innovative solutions that enhance your data analytics capabilities and contribute to the company’s strategic goals.”

4. What skills do you think are essential for a Machine Learning Engineer at ISS?

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

How to Answer

Identify key skills relevant to the position and explain why they are important.

Example

“I believe essential skills for a Machine Learning Engineer at ISS include proficiency in programming languages like Python and Java, a strong understanding of data preprocessing techniques, and the ability to communicate complex concepts clearly to stakeholders. These skills are crucial for developing effective machine learning models that drive business insights.”