Nutanix Data Science Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Nutanix hiring for Data Science roles
Nutanix is a cloud computing software company known for hyperconverged infrastructure that combines storage, compute and virtualisation into one software platform. It helps enterprises run applications across private, hybrid and multi-cloud environments. Its large India engineering teams hire software engineers, quality engineers, site reliability engineers and support engineers working on systems and distributed software.
For Data Science roles, this Nutanix practice set focuses most on MLOps and Model Deployment (8), Deep Learning and NLP (7), Machine Learning (6), SQL for Data Science (4). It has 2 easy, 28 medium, 10 hard questions, so you can start easy and work up to the harder ones.
Company type
Product
Headquarters
San Jose, California, USA
Main India offices
Bengaluru, Pune
Nutanix Data Science interview process
Online coding assessment
Technical round 1 (DSA)
Technical round 2 (OS, systems or distributed design)
Hiring manager round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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01
Pandas: find customers whose monthly spend increased for three consecutive months.
A stationary series has a constant mean, constant variance and an autocorrelation structure that does not change over time. Classical models like ARMA assume stationarity because they learn fixed relationships between past and present values; if the mean keeps drifting, those relationships break. You usually make a series stationary by differencing to remove trend, seasonal differencing to remove seasonality, and a log transform to stabilise growing variance.
04
Write scikit-learn code to calibrate a gradient boosting classifier's probabilities and check the result.
Bias is error from overly simple assumptions (underfitting). Variance is sensitivity to the training data (overfitting). More model complexity lowers bias but raises variance — aim for the lowest total error using cross-validation and regularization.
06
Write Python code to compute the Population Stability Index for a feature to detect drift.
Frequentists treat parameters as fixed and use long-run frequencies; Bayesians treat parameters as distributions updated with data via priors and posteriors, giving direct probability statements.
09
What is the difference between semantic, instance and panoptic segmentation?
Semantic segmentation gives each pixel a class, such as road or car, but does not separate two cars. Instance segmentation separates individual objects of countable classes, giving each car its own mask. Panoptic segmentation combines both: every pixel gets a class, and countable objects also get an instance ID, while background stuff like sky stays semantic. Autonomous driving and robotics often need panoptic or instance output.
10
Your deployed model gives different predictions than the same model in your notebook. Why?
Usually preprocessing differs between training and serving. Check that the same pipeline object was saved, not just the estimator, and that category order, missing value handling and scaling match. Compare library versions, feature types like strings versus integers, column order, and time zones in date features. Send the same few rows through both paths and compare features step by step to find the first mismatch.
11
Your daily sales forecast badly misses the Diwali spike every year. How do you fix it?
The model is treating Diwali as noise because the festival moves on the calendar and a fixed yearly seasonality does not align. Add explicit holiday features: a flag for Diwali and a few days before and after, since buying starts early. Include other regional festivals and sale events too. Models like Prophet or gradient boosting with holiday features handle this well. Then backtest specifically on past festive periods to confirm the fix.
12
Write SQL to find the median order value per city.
SELECT city,
PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY amount) AS median_amount
FROM orders
GROUP BY city;
-- PERCENTILE_CONT interpolates; PERCENTILE_DISC returns an actual value
13
Tell me about a time you had to learn a new deep learning framework or tool quickly.
Situation: my internship project used PyTorch, but I had only used Keras. Task: build a text classifier within two weeks. Action: I followed the official tutorials for two days, rebuilt a small Keras model in PyTorch to compare, and asked a senior to review my training loop. Result: I delivered the model on time with 88 percent F1. I learned that rebuilding something familiar is the fastest way into a new tool.
14
Your SQL result does not match the number on the official dashboard. How do you resolve it?
I would first align definitions: date range, time zone, filters like test users or cancelled orders, and whether the metric counts users or events. Then compare at a smaller grain, by day or by city, to find where the gap starts. I would read the dashboard's underlying query and check data refresh times. Once found, I would document the agreed definition so the same mismatch does not repeat.
15
You must run object detection on an edge camera at 30 frames per second. How do you meet that target?
Start with a lightweight one-stage detector at the smallest input size that keeps acceptable accuracy. Optimise it: export to ONNX, then use a hardware runtime like TensorRT or OpenVINO, with FP16 or INT8 quantisation calibrated on real images. Prune or distil from a larger model if needed. Profile the full pipeline, including decoding and preprocessing, and run detection every few frames with tracking in between if compute is tight.
16
Write Python code to calculate the sample size per group needed to detect a lift from 5 percent to 5.5 percent conversion.
Heterogeneous treatment effects mean the change helps some segments more than others, for example new users but not repeat users. Finding them can guide targeting. The risk is that slicing by many segments creates false discoveries, and each segment has less power, so differences may be noise. Pre-register key segments, correct for multiple comparisons, and validate any surprising segment effect in a follow-up test before acting on it.
Gradient boosting builds trees one after another. It starts with a simple prediction, like the mean. Each new small tree is fitted to the negative gradient of the loss, which for squared error is just the residuals. Its predictions are multiplied by a learning rate and added to the model. Repeating this slowly reduces error. Key settings are number of trees, learning rate, tree depth and subsampling, which together control overfitting.
19
You must classify support messages into 40 categories, but some categories have only 20 examples. What do you do?
Start with a pretrained sentence embedding model plus a simple classifier, or fine-tune a small transformer with class weights. For the rare categories, try few-shot approaches such as SetFit or an LLM with examples in the prompt, and generate or collect more examples. Merge categories that are rarely distinguishable. Report per-class F1 rather than overall accuracy, and route low-confidence predictions to a human queue.
Data augmentation creates new training examples by applying realistic changes to existing ones. For images this means flips, rotations, crops and brightness changes. For text it can mean synonym replacement, back-translation or random word dropping. For audio, adding noise or shifting pitch. It improves generalisation when data is limited. Changes must keep the label correct; flipping a digit 6 can turn it into a 9.
21
Write Python code that validates prediction requests with Pydantic before passing them to a model.
Define churn and the prediction window → build usage and billing features → handle imbalance → train gradient boosting → evaluate with PR-AUC and lift → explain with SHAP → deploy as batch scoring or an API → monitor drift → hand top-risk users to the retention team.
Convolution filters slide over the input to detect local patterns, pooling reduces size, and deeper layers combine edges into shapes and objects. Used mainly for images.
25
What is the difference between hard voting and soft voting in ensembles?
In hard voting each model predicts a class and the majority wins. In soft voting each model gives class probabilities, they are averaged, and the class with the highest average wins. Soft voting usually works better because it uses each model's confidence, but it needs calibrated probabilities from all models. Hard voting is simpler and works even with models that only output labels.
26
What are interaction features and when do they help?
Interaction features combine two or more variables, such as price times quantity, or income divided by number of dependents. They help linear models capture effects where one variable changes the impact of another. Tree models can learn some interactions themselves, but explicit ratios often still help. Too many automatic interactions explode the feature count and overfit, so build them from domain logic and validate each one.
27
Tell me about a time you deployed a model under a tight deadline.
Situation: a pricing model had to go live before a sale in ten days. Task: deploy safely in that window. Action: I cut scope to batch scoring instead of real-time, reused our standard Docker and Airflow template, wrote tests for the key features and set up a dashboard for score distribution. Result: it went live two days early and ran without incidents during the sale. Real-time scoring followed a month later.
28
Your LLM app takes 8 seconds to respond and users complain. What do you do?
Break down where the time goes: retrieval, model time to first token and generation length. Stream tokens so users see output immediately. Cut prompt and output length, run retrieval and other calls in parallel, and cache frequent answers. Use a smaller or faster model for simple requests, and avoid chains of several LLM calls where one would do. Set a latency budget per step and monitor p95, not just averages.
BERT is an encoder-only transformer that reads text in both directions at once. It was pretrained with masked language modelling, where about 15 percent of tokens are hidden and the model predicts them, plus next sentence prediction in the original version. This gives contextual embeddings, so a word's vector depends on its sentence. You fine-tune BERT with a small task head for classification, NER or question answering.
It computes the gradient of the loss with respect to every weight by applying the chain rule backwards through the network, so gradient descent can update them.
It tries candidate splits and picks the one that most reduces impurity — Gini or entropy (information gain) for classification, variance / MSE for regression.
32
Pandas: find the top 3 products by revenue in each region.
NULL means unknown, so any comparison with NULL, even NULL = NULL, gives unknown and the row is filtered out. Use IS NULL or IS NOT NULL instead. Aggregates like SUM, AVG and COUNT(column) skip NULLs, so AVG may be computed over fewer rows than you expect. NOT IN with a subquery that contains a NULL returns no rows. COALESCE replaces NULLs with a default value.
Evaluate retrieval and generation separately. For retrieval, build a set of real questions with known relevant documents and measure recall at k and mean reciprocal rank. For generation, measure faithfulness to retrieved context, answer correctness and relevance, using human review on a sample and an LLM-as-judge with a clear rubric at scale. Track latency and cost too. Rerun the same evaluation set after every change to prompts, chunking or models.
36
Write NumPy code to compute the Dice coefficient between a predicted and a true binary mask.
I would start from the team's pain points, then provide shared building blocks: a standard project template, managed notebooks with access to the warehouse, experiment tracking and a model registry, an orchestrator like Airflow for pipelines, a feature store if real-time use cases exist, a standard serving path using containers, and default monitoring. Prefer managed services over custom builds, and add self-serve documentation so teams do not depend on one platform engineer.
38
Write Python code to run an ADF stationarity test and difference the series if needed.
from statsmodels.tsa.stattools import adfuller
def is_stationary(s, alpha=0.05):
p_value = adfuller(s.dropna())[1]
return p_value < alpha
series = s.copy()
d = 0
while not is_stationary(series) and d < 2:
series = series.diff()
d += 1
print('differencing order d =', d)
Term Frequency × Inverse Document Frequency: a word scores high if it is frequent in one document but rare across all documents. A classic way to turn text into numeric features.
40
Write SQL to calculate month-over-month revenue growth.
WITH monthly AS (
SELECT DATE_TRUNC('month', order_date) AS month, SUM(amount) AS revenue
FROM orders
GROUP BY 1
)
SELECT month, revenue,
LAG(revenue) OVER (ORDER BY month) AS prev_revenue,
ROUND(100.0 * (revenue - LAG(revenue) OVER (ORDER BY month))
/ NULLIF(LAG(revenue) OVER (ORDER BY month), 0), 2) AS mom_pct
FROM monthly;
Tips to crack the Nutanix Data Science interview
Revise operating systems, virtualisation, storage and networking fundamentals, as systems knowledge is important for Nutanix engineering roles.
Practise DSA problems and be ready to write correct code in C++, Go, Java or Python under time pressure.
Learn distributed systems basics such as replication, consistency and fault tolerance for deeper technical discussions.
Keep a one-minute story ready for every project on your CV: problem, your part, tools, result.
Practise the hard questions on this page aloud; Nutanix interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Nutanix Data Science interview?
Candidates usually go through 4 stages: Online coding assessment; Technical round 1 (DSA); Technical round 2 (OS, systems or distributed design); Hiring manager round. The exact rounds change with the role, team, location and your experience, so confirm the process with the recruiter.
Which topics should I prepare for the Nutanix Data Science interview?
This practice set leans on MLOps and Model Deployment (8), Deep Learning and NLP (7), Machine Learning (6), SQL for Data Science (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Nutanix interviewers ask you to walk through real work.
Are these the exact questions asked at Nutanix?
No. These are curated practice questions that match the skills and difficulty usually tested in product interviews like Nutanix's. They are not a leaked or verified list of past questions.
Where does Nutanix hire Data Science professionals in India?
Nutanix has large teams in Bengaluru, Pune. Openings and locations change often, so check the Nutanix careers page and filter by city and experience.