Infor Data Scientist interview questions & guide 2026

Our interview process is designed to evaluate your technical depth, problem-solving structure, and cultural alignment. The questions below reflect patterns seen in recent interviews and are intended to help you understand the breadth of topics we cover.

Technical and Machine Learning Fundamentals

These questions assess your foundational knowledge of statistical modeling, algorithm selection, and data processing.

• Explain the bias-variance tradeoff and how you manage it in practice.
• How do you handle missing data or imbalanced datasets in a production environment?

Success at Infor requires a balance of technical prowess and clear, structured communication. Think of your preparation as a way to demonstrate that you can act as a bridge between raw data and business value.

Role-Related Knowledge – We look for a deep understanding of core machine learning concepts and proficiency in the tools of the trade. You must be able to discuss the "why" behind your choices, not just the "how."

Problem-Solving Ability – During your use case assessments, we focus on your methodology. We evaluate your ability to scope a problem, clean and prepare data, and justify your modeling choices logically.

Communication and Collaboration – You will often work alongside engineers and product managers. We look for your ability to distill complex insights into clear narratives that help others make informed decisions.

The recruitment process at Infor is structured to be thorough, ensuring that both the candidate and the team are a strong match. While the exact number of stages can vary depending on the specific team, you should generally expect a series of screenings followed by a rigorous technical evaluation.

Expect a journey that begins with HR, moves into technical discussions with your potential peers or managers, and culminates in a practical assessment. We value transparency and professionalism, and we aim to provide a consistent, respectful experience throughout your time with us.

This timeline illustrates the progression from initial HR screening to final leadership discussions. Use this to pace your study schedule, ensuring you have time to focus on both technical fundamentals and the nuances of the take-home use case.

Technical Depth and Modeling

We evaluate your ability to handle the full lifecycle of a data project. You should be prepared to discuss your past projects in depth.

Be ready to go over:

• Feature Engineering – Techniques for creating meaningful features from raw inputs.
• Model Evaluation – Metrics beyond accuracy, such as F1-score, precision-recall, and AUC-ROC.

As a Data Scientist, your primary responsibility is to translate business needs into data solutions. You will spend your time cleaning large datasets, building and validating models, and collaborating with software engineers to integrate these models into Infor’s products.

You will be expected to:

• Translate business requirements into technical specifications.
• Collaborate with cross-functional teams to identify opportunities for automation and optimization.
• Communicate findings to stakeholders, ensuring that the business understands the potential impact of your models.
• Maintain a high standard of code quality and documentation for all analytical work.

We seek candidates who combine academic rigor with practical, hands-on experience.

• Must-have skills – Proficiency in Python or R, strong grasp of SQL, and deep experience with machine learning libraries (e.g., Scikit-learn, TensorFlow, or PyTorch).
• Nice-to-have skills – Experience with cloud platforms like AWS or Azure, knowledge of big data frameworks like Spark, and familiarity with MLOps tools.
• Experience level – A strong background in quantitative fields, with a track record of delivering end-to-end data projects.

Q: How difficult is the technical assessment? A: The technical assessment is designed to be practical. It is meant to test your ability to apply theory to a real-world scenario rather than testing your ability to memorize obscure algorithms.

Q: How long does the entire process take? A: The timeline can vary, but typically it spans 3 to 5 weeks. We prioritize quality and fit over speed.

Q: Is there a focus on specific industry domains? A: Yes, because Infor creates software for specific industries, having some familiarity with supply chain, manufacturing, or ERP systems can be a significant advantage.

Q: What is the most common reason for a candidate not moving forward? A: Often, it is the inability to explain the "business value" of a technical decision or failure to communicate the logic behind the approach during the use case phase.

• Structure your answers: Use the STAR method (Situation, Task, Action, Result) for behavioral questions to keep your responses focused.
• Be honest about limitations: If you don't know the answer to a technical question, explain how you would go about finding the answer rather than guessing.
• Ask meaningful questions: Use your interview time to learn about the team’s current data challenges; this shows you are already thinking like a member of the team.

Joining Infor as a Data Scientist is an opportunity to work on complex, industry-defining software. Your success depends on your ability to combine technical expertise with a clear, business-oriented mindset. Focus your preparation on mastering the fundamentals, refining your problem-solving process, and being ready to communicate your work clearly to both technical and non-technical stakeholders.

You have the skills to make a significant impact here. Take the time to review your past projects, prepare your narratives, and approach each interview stage as a collaboration. We look forward to seeing how your unique perspective can help drive the future of Infor.