Getting Ready for Your IRI Interview: 24 Common Questions and How to Answer Them

Landing an interview at IRI (Information Resources Inc) is exciting! As a leading global provider of big data, predictive analytics, and forward-looking insights, IRI is an prestigious company to work for

However their interview process can be tough. IRI interviews often involve multiple rounds including phone screens case studies, and various behavioral and technical questions.

Not to worry! In this article I’ll walk you through the most common IRI interview questions and tips on how to prepare winning responses.

Overview of the IRI Interview Process

Before we get into specific questions, let’s take a quick look at what to expect during an IRI interview:

• Initial Phone Screen: 30-45 minute call with an IRI recruiter reviewing your resume and experience. Aims to determine basic fit for the role.

• Technical Phone Interview: 1 hour call focused on your relevant technical skills for the position like SQL, Python, R, etc. May involve a short coding exercise.

• Case Study: Many jobs require you to do a case study to show how analytical you are. This can be done on your own time before the in-person interviews.

• In-Person Interviews: Typically involves 4-5 rounds of interviews either in-office or virtual. Includes both behavioral and technical questions with hiring managers and potential teammates.

• Checking Professional References: As a last step before making an offer, IRI checks professional references. Prepare 2-3 positive references.

Now let’s look at some of the most frequently asked questions during the IRI interview process.

Common IRI Interview Questions and How to Answer Them

1. Walk me through your experience working with data analytics.

IRI looks for candidates with substantive experience in data analytics. Be prepared to discuss your specific experience:

• Tools used: SQL, Python, R, Excel, etc.

• Techniques applied: Statistical analysis, machine learning, predictive modeling, etc.

• Business impact: What insights did you discover? How did your work help solve problems or influence decisions? Quantify with metrics if possible.

• Communication: How did you present technical findings to stakeholders? What visualization or storytelling techniques did you use?

2. How would you analyze a large set of data to uncover insights?

• Highlight your structured, methodical approach to data analysis.

• Discuss techniques used like exploratory analysis, statistical testing, clustering, etc.

• Emphasize importance of understanding business goals to guide relevant analysis.

• Share how you translate findings into actionable recommendations for stakeholders.

3. Tell me about a time you had to simplify complex technical concepts for business executives.

• Share a specific example of presenting complex analysis to non-technical audience.

• Emphasize how you translated jargon and made concepts understandable.

• Discuss use of storytelling techniques, visualizations, analogies, etc. to aid comprehension.

• Highlight positive outcomes of getting key leaders to understand and buy into your analysis.

4. How do you handle situations where you are required to manage multiple projects with competing deadlines?

• Discuss your time management, organization, and prioritization strategies.

• Provide a detailed example demonstrating how you juggle multiple deliverables.

• Highlight specific tools and tactics used to stay on track like shared calendars, status reports, open communication, etc.

• Emphasize ability to complete high-quality work efficiently under pressure. Share examples and outcomes.

5. Tell me about a time you took initiative to drive innovation or process improvements in your department or company.

• Share a specific example where you went beyond your formal responsibilities to create positive change.

• Discuss how you identified opportunities for innovation or process improvements.

• Explain steps taken to design and implement solutions.

• Emphasize analytical thinking, creativity, drive, and leadership capabilities.

• Share measurable outcomes that benefited the company.

6. Describe a situation where you had to collaborate with team members from diverse backgrounds. How did you ensure an inclusive environment?

• Discuss working with cross-functional or international teams.

• Share how you embraced unique perspectives to drive innovation.

• Explain tactics used to foster open communication and understanding among diverse colleagues.

• Highlight adaptability, empathy, and respect shown to differences.

• Share example of how team diversity led to better solutions.

7. How would you go about identifying growth opportunities or new market insights for a client?

• Discuss market and competitive research strategies like SWOT, PESTEL, and Porter’s Five Forces.

• Explain how to leverage primary and secondary research to understand customer needs, trends, etc.

• Share tools and tactics used for data analysis to uncover trends and gaps.

• Emphasize ability to translate research findings into strategic recommendations or new product/market opportunities.

8. Walk me through how you would solve a challenging data analytics problem.

• Outline your structured process for tackling complex data challenges.

• Share how you would clarify business goals, identify metrics, clean and prepare data, conduct analysis, interpret findings, and presents insights.

• Discuss statistical, machine learning, or other advanced techniques you have applied to drive insights.

• Provide real examples of how your data analysis solved problems for an employer or client.

9. How do you stay on top of the latest developments and trends in the data analytics field?

• Mention daily habits like reading industry publications, blogs, forums etc.

• Discuss attending conferences and seminars to connect with peers and experts.

• Share how you experiment with new tools by taking online courses and personal projects.

• Give examples of how you’ve applied cutting-edge techniques like AI/ML to drive innovation in your work.

• Demonstrate passion for continuous learning.

10. Why do you want to work for IRI specifically?

• Show you’ve researched IRI and know their products, mission, and values.

• Share why you’re passionate about IRI’s focus areas like retail, CPG, media, and healthcare analytics.

• Discuss how your background and skills make you a great culture fit.

• Convey genuine excitement to gain experience with IRI’s data resources, analytics capabilities, and industry experts.

Technical IRI Interview Questions

Expect more rigorous technical questioning if interviewing for data science, analytics, engineering, or other technical roles at IRI. Some common technical questions include:

11. What’s the difference between descriptive, predictive, and prescriptive analytics?

• Descriptive: Visualizes and summarizes what happened using dashboards, reports, etc.

• Predictive: Uses historical data to forecast what could happen in future using models.

• Prescriptive: Recommends specific actions to take to achieve desired outcomes.

12. How would you build a machine learning model? Walk through the full process.

• Discuss key steps like data collection, preprocessing, model selection, training/validation, testing, and deployment.

• Highlight experience with ML algorithms like logistic regression, random forest, neural networks etc.

• Share how you have optimized models for accuracy and performance.

13. Explain how you would perform A/B testing for a mobile app feature rollout.

• Discuss importance of identifying key metrics and setting hypotheses.

• Explain how you would design the experiment by splitting users into control/variant groups.

• Share how you would track metrics like clicks, conversions, retention etc. and analyze results.

• Discuss how you would interpret data to decide whether to roll out the feature.

14. How would you detect anomalies or fraudulent activities in a large dataset?

• Share techniques like clustering, isolation forests, local outlier factors, etc.

• Discuss process for exploring data, identifying anomalies, and developing algorithms to flag them.

• Provide examples of how you’ve built anomaly or fraud detection systems.

15. Can you explain what sampling methods you have used in data analysis?

• Discuss techniques like simple random, stratified, cluster, and convenience sampling.

• Share when you would use each method based on factors like population size, variance, etc.

• Provide examples of analyzing large datasets by selecting representative samples.

IRI Case Study Interview Questions

Many IRI interviews include a case study exercise to assess your analytical thinking and problem-solving abilities. Some examples of case study questions:

16. How would you design a customer segmentation model for a retailer’s loyalty program?

• Discuss collecting relevant customer data like demographics, purchase history, engagement metrics, etc.

• Share how you would preprocess data and explore relationships between variables.

• Explain how you would apply techniques like clustering algorithms to group customers into segments.

• Discuss interpreting and profiling different segments to develop targeted strategies.

17. Our client is launching a new CPG product. How would you forecast first year sales?

• Discuss researching factors like market size, competitor products, pricing, promotions, distribution strategy etc.

• Share how you would leverage statistical modeling or ML techniques to forecast base