Informa Tech Data Scientist Interview Questions + Guide in 2025

Informa Tech is a leading international events, intelligence, and scholarly research group, dedicated to providing specialized insights and connections that empower businesses and professionals in niche markets.

As a Data Scientist at Informa Tech, you will play a crucial role in driving data-driven decisions and enhancing customer engagement through the analysis of complex datasets. Your key responsibilities will include designing and implementing data models, conducting statistical analyses, and leveraging machine learning algorithms to extract valuable insights. A strong proficiency in statistics is essential, as you will be expected to apply statistical methods to interpret data and inform strategic initiatives. Familiarity with programming languages such as Python will be important for developing analytical tools and automating data processing tasks. Additionally, you will collaborate with cross-functional teams to understand data requirements and ensure the quality and integrity of the data being used.

A great fit for this role would be someone who is not only technically skilled but also possesses excellent problem-solving capabilities and a keen attention to detail. Your ability to communicate complex findings clearly and effectively will be vital in influencing key business decisions. Moreover, a passion for continuous learning and adapting to the evolving data landscape aligns perfectly with Informa Tech's commitment to innovation and excellence.

This guide will equip you with the knowledge and insights necessary to prepare for your interview, helping you to stand out as a candidate who can contribute meaningfully to Informa Tech's mission.

The interview process for a Data Scientist role at Informa Tech is structured to assess both technical expertise and cultural fit within the organization. It typically consists of several key stages:

1. Initial Screening

The process begins with an initial phone screening, usually conducted by a recruiter. This conversation is designed to gauge your interest in the role, discuss your background, and evaluate your fit for the company culture. Expect questions that explore your motivations for applying and your understanding of the data science landscape.

2. Assessment Test

Following the initial screening, candidates may be required to complete an assessment test. This test often includes tasks that evaluate your proficiency in data analysis, such as advanced Excel tasks or coding challenges. The assessment is designed to provide insight into your technical skills and problem-solving abilities, which are crucial for the role.

3. Technical Interviews

Candidates typically undergo two or more technical interviews. These interviews may be conducted remotely or in-person and focus on your technical knowledge and experience in data engineering, statistics, and algorithms. You may be asked to discuss past projects, demonstrate your understanding of data acquisition and transformation processes, and showcase your coding skills, particularly in Python and SQL.

4. Behavioral Interviews

In addition to technical assessments, behavioral interviews are a significant part of the process. These interviews aim to understand how you handle various work situations, your approach to teamwork, and your problem-solving strategies. Expect questions that require you to provide examples from your past experiences, particularly in managing tight deadlines or navigating complex projects.

5. Final Interview

The final stage often involves a panel interview with key stakeholders, including the hiring manager and other team members. This round is an opportunity for you to demonstrate your technical leadership capabilities and discuss how you would contribute to the team’s goals. It may also include discussions about your vision for data engineering and how you would align with Informa's strategic objectives.

As you prepare for your interviews, consider the types of questions that may arise in each of these stages, particularly those that relate to your technical skills and past experiences.

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

Understand the Interview Process

Informa Tech's interview process is generally straightforward but can vary in length and complexity. Expect an initial phone screening followed by multiple rounds of interviews, which may include technical assessments and behavioral questions. Familiarize yourself with the structure of the interviews, as this will help you manage your time and prepare effectively. Be ready to discuss your experience in data engineering, particularly focusing on data acquisition, transformation, and distribution.

Showcase Your Technical Expertise

Given the emphasis on technical skills in the role, ensure you are well-versed in key areas such as statistics, algorithms, and Python. Be prepared to discuss specific projects where you applied these skills, particularly in data engineering contexts. Highlight your experience with AWS services, Redshift, and any big data technologies you have worked with. Demonstrating your technical knowledge will not only show your capability but also your enthusiasm for the role.

Prepare for Behavioral Questions

Informa values candidates who can articulate their experiences and how they align with the company's culture. Prepare to answer behavioral questions that explore your problem-solving abilities, teamwork, and leadership experiences. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you provide clear and concise examples that reflect your skills and adaptability.

Emphasize Communication Skills

Strong communication skills are crucial for a Data Scientist at Informa, especially when collaborating with cross-functional teams. Be prepared to discuss how you have effectively communicated complex data insights to non-technical stakeholders in the past. This will demonstrate your ability to bridge the gap between technical and non-technical team members, a valuable trait in any data-driven organization.

Engage with the Interviewers

During your interviews, take the opportunity to ask insightful questions about the team dynamics, company culture, and the specific challenges the Data Value Chain team is facing. This not only shows your genuine interest in the role but also helps you assess if Informa is the right fit for you. Remember, interviews are a two-way street, and engaging with your interviewers can leave a positive impression.

Be Patient and Follow Up

While the interview process at Informa can sometimes be drawn out, it’s important to remain patient and professional. If you haven’t heard back after your interviews, don’t hesitate to follow up politely. This shows your continued interest in the position and can help keep you on their radar.

Reflect Company Values

Informa places a strong emphasis on diversity, inclusion, and employee development. Be sure to reflect these values in your responses and demonstrate how you can contribute to a supportive and innovative work environment. Highlight any experiences that showcase your commitment to these principles, as they align closely with the company’s mission.

By following these tailored tips, you can approach your interview with confidence and a clear understanding of what Informa Tech is looking for in a Data Scientist. Good luck!

In this section, we’ll review the various interview questions that might be asked during an interview for a Data Scientist role at Informa Tech. The interview process will likely focus on your technical expertise in data engineering, statistical analysis, and problem-solving abilities, as well as your capacity to work collaboratively within a team. Be prepared to discuss your past experiences and how they relate to the responsibilities outlined in the job description.

Technical Skills

1. Can you describe your experience with data acquisition and the tools you have used?

This question aims to assess your familiarity with data acquisition processes and tools relevant to the role.

How to Answer

Discuss specific tools and technologies you have used for data acquisition, emphasizing your understanding of the data sources and the methods you employed to gather data.

Example

“I have extensive experience using tools like Apache NiFi and Talend for data acquisition. In my previous role, I designed data pipelines that integrated various data sources, ensuring that the data was collected efficiently and accurately for further analysis.”

2. How do you ensure data quality and consistency in your projects?

This question evaluates your approach to maintaining high data quality standards.

How to Answer

Explain the processes and techniques you implement to validate and clean data, as well as any tools you use to monitor data quality.

Example

“I prioritize data quality by implementing automated validation checks during the data ingestion process. I also use tools like Great Expectations to define expectations for data quality and run regular audits to ensure consistency across datasets.”

3. Describe a project where you had to transform data for analysis. What challenges did you face?

This question seeks to understand your experience with data transformation and the obstacles you encountered.

How to Answer

Share a specific project example, detailing the transformation processes you used and how you overcame any challenges.

Example

“In a recent project, I had to transform raw sales data into a structured format for analysis. One challenge was dealing with missing values, which I addressed by implementing imputation techniques and ensuring that the final dataset met our analysis requirements.”

4. What is your experience with AWS services, particularly Redshift?

This question assesses your technical knowledge of cloud services and data warehousing.

How to Answer

Discuss your experience with AWS and Redshift, including specific projects where you utilized these technologies.

Example

“I have worked extensively with AWS Redshift for data warehousing. In my last role, I migrated our on-premise data warehouse to Redshift, which improved our query performance significantly. I also optimized our ETL processes to ensure efficient data loading into Redshift.”

5. Can you explain the importance of data governance and how you implement it in your work?

This question evaluates your understanding of data governance principles.

How to Answer

Discuss the key aspects of data governance and how you ensure compliance and data security in your projects.

Example

“Data governance is crucial for maintaining data integrity and compliance. I implement governance by establishing clear data ownership, creating data access policies, and ensuring that all data handling practices adhere to industry regulations.”

Behavioral Questions

1. Describe a time when you had to work under tight deadlines. How did you manage the situation?

This question assesses your ability to handle pressure and meet deadlines.

How to Answer

Share a specific example of a project with tight deadlines, focusing on your time management and prioritization strategies.

Example

“In a previous project, we had a tight deadline to deliver insights for a client presentation. I prioritized tasks by breaking down the project into manageable parts and delegated responsibilities to team members, which allowed us to meet the deadline without compromising quality.”

2. How do you approach collaboration with cross-functional teams?

This question evaluates your teamwork and communication skills.

How to Answer

Discuss your strategies for effective collaboration and how you ensure alignment with other teams.

Example

“I believe in open communication and regular check-ins with cross-functional teams. In my last project, I organized weekly meetings to discuss progress and gather feedback, which helped us stay aligned and address any issues promptly.”

3. Can you give an example of a project where you had to analyze complex data? What was your approach?

This question seeks to understand your analytical skills and problem-solving approach.

How to Answer

Describe a specific project, detailing the complexity of the data and the analytical methods you employed.

Example

“I worked on a project analyzing customer behavior data, which involved multiple data sources and complex relationships. I used Python and SQL to clean and analyze the data, applying machine learning techniques to uncover patterns that informed our marketing strategy.”

4. What interests you about this role at Informa Tech?

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

How to Answer

Express your enthusiasm for the role and how it aligns with your career goals and values.

Example

“I am excited about the opportunity at Informa Tech because I admire the company’s commitment to providing valuable insights through data. I believe my skills in data engineering and my passion for driving data-driven decisions align perfectly with the goals of the IIRIS platform.”

5. How do you handle feedback and criticism in your work?

This question assesses your receptiveness to feedback and your ability to grow professionally.

How to Answer

Share your perspective on feedback and provide an example of how you have used it to improve your work.

Example

“I view feedback as an essential part of my professional growth. In a previous role, I received constructive criticism on my data visualization techniques. I took that feedback to heart, enrolled in a workshop, and subsequently improved my visualizations, which were well-received by my team.”