Ntt Data Enterprise Services Data Scientist interview typically runs 2 rounds: HR conversation and technical round. It usually takes about 1-2 weeks and is described as pleasant and low-pressure.
$107K
Avg. Base Comp
$134K
Avg. Total Comp
2-3
Typical Rounds
1-2 weeks
Process Length
We've seen a very clear pattern in this process: the team is less interested in polished buzzwords and more interested in whether candidates can explain their own work with precision. The candidate who received an offer described the early conversation as a fit check that dug into university projects, role ownership, and motivation, which tells us they want people who can narrate their experience cleanly and credibly. That same theme carried into the technical discussion, where the focus stayed on past projects and how the candidate thought through them rather than on abstract theory alone.
A recurring signal is the company’s preference for practical machine learning judgment. Multiple details point to a strong emphasis on fundamentals applied to real problems: imbalanced datasets, metric choice, precision versus recall, and even activation functions in a computer vision context. We also noticed a specific interest in generative AI, which suggests they are looking for candidates who can connect newer methods to business use cases without sounding superficial. In our view, the candidates who do best here are the ones who can move comfortably from a project example to the underlying ML reasoning and back again.
Synthetized from 1 candidates reports by our editorial team.
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Real interview reports from people who went through the Ntt Data Enterprise Services (Previously Optimal Solutions) process.
The interview was actually pretty pleasant and low-pressure, which surprised me a bit for a Data Scientist role. The first step was an HR conversation where they introduced the company and the position, then asked about my university path and why I was interested in them. It felt more like a fit check than a formal screening, and they spent time trying to understand the group projects I had done and what my role was in each one.
The technical round came after that and was more focused on my skills and past projects, both academic and work-related. They asked me what machine learning models I had studied, and then moved into a short exercise plus some practical questions around handling an imbalanced dataset. I was asked which evaluation metric I would use in that case and to explain precision and recall clearly. There was also a computer vision angle tied to one of my university projects, including activation functions for an object detection problem. The biggest emphasis overall was on machine learning and deep learning, with a particular interest in generative AI, so it helped to be ready to talk beyond just theory and connect concepts back to real projects. I ended up getting the offer, and my main takeaway was that they cared a lot about how well I could explain my work and reason through fundamentals rather than just recite definitions.
Prep tip from this candidate
Be ready to explain your past projects in detail, especially your specific role, and practice discussing imbalanced datasets, precision vs. recall, and which metric you’d choose. Since generative AI came up as a focus, it would also help to review the ML/DL models you’ve studied and be able to connect them to a computer vision or object detection project.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Ntt Data Enterprise Services (Previously Optimal Solutions)
Addressing imbalanced data in machine learning through carefully prepared techniques.
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Synthesized from candidate reports. Individual experiences may vary.
The first step is a low-pressure HR conversation that introduces the company and the Data Scientist role. Expect questions about your university path, why you are interested in the company, and a discussion of your group projects and your specific contributions.
The technical round focuses on your machine learning and deep learning background, along with both academic and work projects. You may be asked to name models you have studied, complete a short exercise, and answer practical questions such as how to handle an imbalanced dataset, which evaluation metric to use, and how to explain precision and recall.
This part of the process appears to be embedded within the technical interview and emphasizes how well you can explain your past work and reason through core concepts. Interviewers may probe computer vision topics from university projects, including activation functions for object detection, and show interest in areas like generative AI.