Tredence Data Scientist candidates describe an online assessment, a communication test, two technical interviews and an HR round. Expect SQL, coding, ML and statistics, plus detailed questions on your own projects.
$149K
Avg. Base Comp
$170K
Avg. Total Comp
5 rounds
Typical Rounds
Not reported
Process Length
Candidates who describe the Tredence Data Scientist process most often report a staged flow: an online assessment, a separate communication test, two technical interviews and an HR conversation. Three candidates count five rounds in total, though not every account lists each stage.
The online assessment is broad. Candidates describe multiple-choice questions on aptitude, probability, matrix math and data science, followed by two or three coding problems. Reported topics include string and array problems, SQL queries and a spiral-order matrix traversal. One candidate found the lengthy calculations without a calculator the hardest part, so time management on the assessment matters.
The communication test is not technical. Candidates describe grammar questions, reading sentences aloud, repeating audio and speaking for about a minute. One candidate links it to the client-facing nature of the work.
In the technical interviews, the most consistent theme is your own resume projects. Candidates say interviewers asked them to explain what they actually did, why they chose a model and which evaluation metric they used. Reported theory questions include R2 versus adjusted R2, CNN architecture, classification metrics, logistic regression and probability. Others mention SQL joins, NLP and how to troubleshoot a failed data pipeline. One candidate also got an integer digit-rearrangement problem in the technical rounds.
Outcomes are mixed: some candidates accepted offers and others did not. One candidate describes rescheduled rounds and last-minute changes, so it helps to confirm timelines with the recruiter.
Synthesized from 11 candidate reports by our editorial team.
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Real interview reports from people who went through the Tredence process.
The whole process felt pretty luck-based, and I'm not even exaggerating. Depending on which questions you drew, you could get something straightforward or get hammered by a DP problem. I managed to land in the better half.
My coding question was an optimization puzzle: three arrays representing satisfaction level, calories, and cost for various dishes. The task was to find the minimum cost such that you hit a satisfaction threshold without exceeding a calorie limit. Textbook DP setup. I coded it up and passed all 10 test cases. That question alone was worth 20 marks.
Beyond the main coding problem, they covered ML fundamentals — specifically linear regression and logistic regression — plus SQL and Python questions that tested actual problem-solving. I also had to walk through my internship and project experience in detail, explaining what I actually did and what I learned.
The interview had multiple stages. There was an initial online assessment round with MCQs on data structures, SQL, and programming basics, followed by technical rounds and what they called a client round. The overall experience seemed to depend a lot on which interviewer I got and which questions I drew, but I managed to navigate it okay and got the offer.
Prep tip from this candidate
Focus on DP-based optimization problems and be comfortable explaining linear and logistic regression from scratch — these came up consistently across technical rounds.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Tredence
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Minimum Change | |
| Bagging vs Boosting | |
| One Million Rides | |
| Hurdles In Data Projects | |
| CNNs vs Intensity-Based Features | |
| Cumulative Sales By Product | |
| RAG Strict Source Control | |
| Count Transactions | |
| Bias vs. Variance Tradeoff | |
| Overfit Avoidance | |
| String Palindromes | |
| Pipeline Transformation Failures | |
| Fixed-Length Arrays: Deletion | |
| Testing Constraints | |
| Logistic Regression from Scratch | |
| Merchant Acquisition | |
| Area Under the ROC Curve | |
| Your Strengths and Weaknesses | |
| Evaluate News | |
| Bias Variance Tradeoff | |
| Analyzing Multiple Data Sources | |
| Employee Salaries | |
| Empty Neighborhoods | |
| Top Three Salaries | |
| Closest SAT Scores | |
| Prime to N | |
| First to Six | |
| Merge Sorted Lists | |
| Experiment Validity |
Synthesized from candidate reports. Individual experiences may vary.
Candidates describe a timed online test, reportedly on HackerEarth or Mettl. Reports mention MCQs on aptitude, probability, matrix math, ML and data structures, followed by two or three coding questions on strings, arrays and SQL. One candidate saw a spiral-order matrix traversal, and another found the lengthy calculations without a calculator the main time pressure.
Candidates describe a separate, non-technical test, reportedly on Mettl, with grammar questions, reading sentences aloud, repeating audio and answering questions about it. One candidate also had to speak for about a minute on a topic. Another felt it screens for clear English because the work involves direct client interaction.
Most candidates report two technical interviews. Interviewers dig into resume projects and model choices, then ask about ML, deep learning, NLP, statistics, probability, Python and SQL. Reported examples include R2 versus adjusted R2, CNN architecture, classification metrics and diagnosing a failed data pipeline. One candidate was asked an integer digit-rearrangement problem in these rounds, and another said a DSA problem can come near the end. Interviewer style reportedly varies.
Candidates with a five-round experience describe a final HR conversation. One called it much easier than the technical stages, covering overall fit, understanding of the role and background. Another candidate's late rounds were rescheduled at short notice, so the final stages can feel uneven.
