Incedo Data Scientist interviews, as two candidates describe them, mix short Python and SQL exercises with core machine learning questions. Here is what they were asked and how to prepare.
$94K
Avg. Base Comp
$117K
Avg. Total Comp
2-4 rounds
Typical Rounds
Not reported
Process Length
Two candidates describe Incedo data science interviews that test breadth over depth, though their experiences differ. The Data Scientist candidate had two roughly 30-minute video rounds on Teams and describes a techno-managerial round as the most important. It had two Python output-based problems, including a list rotation, plus questions on overfitting versus underfitting, random forest, logistic regression and the elbow method. That candidate felt the interviewers wanted clear explanations of fundamentals, not advanced theory. The interviewer also described a telephonic client round and an HR round as next steps.
The Associate Data Scientist candidate describes a faster, more intense format. A same-day online assessment came first. The next day brought a panel with two senior team members and a separate session with the director of data science. Across those interviews, questions jumped between a SQL query on employee salaries, choosing a Power BI visualization, matplotlib, handling categorical data, why overfitting occurs, and detecting anomalies and outliers. There was also an estimation prompt about how many basketballs fit in a room using a tape measure, and a behavioral question about a greatest achievement. This candidate says interviewers probed answers closely.
Both candidates ended without an offer, and both mention poor communication afterward. One was rejected about a week later with no stated reason. The other followed up five times over six weeks without a clear answer. Prepare to explain basic ML concepts aloud and to solve small Python output problems quickly. For estimation questions, talk through your reasoning and state your assumptions as you go.
Synthesized from 2 candidate reports by our editorial team.
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Real interview reports from people who went through the Incedo Inc. process.
I received a call from HR about the Data Scientist opening and confirmed my interest. The first round was a 30-minute interview on Microsoft Teams, and it was fairly straightforward. The second round was originally supposed to be face-to-face in Bangalore, but they later converted it to Teams from their office, and that round also lasted about 30 minutes. After that, the interviewer said the next step would be a telephonic client round, followed by an HR round, so the process felt like it was moving in a pretty structured way.
The techno-managerial round was the most important one for me. I had to solve two Python coding challenges and give the expected output, and one of them was a list rotation problem. On the ML side, they asked me about overfitting and underfitting, random forest, logistic regression, and the elbow method. The questions were not extremely deep, but they did expect clear fundamentals and the ability to explain concepts cleanly. I answered everything, and the panel seemed satisfied during the interview itself.
What surprised me was that after all that, HR informed me about a week later that my candidature had been rejected, without any clear reason. That was frustrating, especially because the panel had already shared details about the client round and the next steps, which made it feel like things were progressing normally. Overall, I’d say the process was moderate in difficulty: basic Python plus core ML concepts, with more emphasis on explaining the basics well than on advanced theory. If you’re preparing for this role, I’d make sure you can confidently explain common ML concepts and do simple Python output-based coding problems quickly.
Prep tip from this candidate
Be ready for short Python output-based problems like list rotation, and review core ML fundamentals such as overfitting vs. underfitting, random forest, logistic regression, and the elbow method. The interview seemed to value clear explanations of basics more than advanced theory.
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Topics based on recent interview experiences.
Featured question at Incedo Inc.
Find the longest increasing subsequence in a list of integers.
| Question | |
|---|---|
| Logistic Regression from Scratch | |
| Random Forest from Scratch | |
| 2nd Highest Salary | |
| Empty Neighborhoods | |
| Top Three Salaries | |
| Merge Sorted Lists | |
| Employee Salaries | |
| Upsell Transactions | |
| First to Six | |
| Longest Streak Users | |
| Rolling Bank Transactions | |
| Customer Orders | |
| Top 3 Users | |
| Comments Histogram | |
| Closest SAT Scores | |
| Last Transaction | |
| Manager Team Sizes | |
| Random SQL Sample | |
| Find the Missing Number | |
| Paired Products | |
| Subscription Overlap | |
| Employee Salaries (ETL Error) | |
| Size of Joins | |
| Monthly Customer Report | |
| Largest Salary by Department | |
| Experiment Validity | |
| Download Facts | |
| Cumulative Distribution | |
| Prime to N |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report different starts. One received an HR call about the opening and confirmed interest. The other, interviewing for an associate-level role, got a recruiter email and was asked to complete an online assessment the same day. Expect quick scheduling and be ready to move fast.
One candidate describes a fairly straightforward 30-minute video interview. The other describes a panel with two senior team members, part of a back-to-back day that covered SQL, Power BI, Python, ML and behavioral questions. The format may vary by role and level, so prepare for both a light screen and a broad technical panel.
One candidate describes a techno-managerial round as the most important. It had two Python problems where the candidate had to give the expected output, one of them a list rotation. It also covered overfitting, underfitting, random forest, logistic regression and the elbow method. Clear explanations seemed to matter more than advanced theory.
The associate-level candidate describes a separate session with the director of data science after the panel. Across that day, the candidate faced problem-solving prompts such as a basketball estimation question and probing follow-ups on reasoning. Practice explaining your assumptions out loud.
One candidate was told a telephonic client round and an HR round would follow, but was rejected about a week later without a stated reason. The other was told to expect a response within a week and followed up repeatedly over six weeks without a clear answer. Both ended without an offer, so expect that feedback may be limited.
