36 practice questions with answers for Data Science interviews in Jaipur.
Data Science jobs in Jaipur
Jaipur has a growing IT and IT-enabled services market, with delivery centres, SaaS start-ups and back-office analytics teams around Sitapura and Mahindra World City. Many candidates here also apply to remote and hybrid roles with Delhi NCR teams.
Below are 36 questions that match the mix of service, product and domain employers hiring data scientists in Jaipur.
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01
Write Python code to count word frequencies in a text and print the top 5 words.
from collections import Counter
import re
def top_words(text, n=5):
words = re.findall(r'[a-z]+', text.lower())
return Counter(words).most_common(n)
print(top_words('Data science is fun and data is everywhere'))
02
What is the difference between saving a model with pickle, joblib and ONNX?
pickle serialises Python objects; it is simple but tied to Python and library versions, and loading untrusted pickle files is a security risk. joblib is similar but more efficient for objects with large NumPy arrays, like scikit-learn models. ONNX is an open, language-neutral format that runs in optimised runtimes across languages and hardware, useful for fast serving. Always store library versions alongside pickle or joblib files.
During training it randomly switches off a fraction of neurons so the network cannot depend on any single unit, acting as a regulariser against overfitting.
04
Tell me about a time your forecast was badly wrong. How did you handle it?
Situation: my weekly demand forecast under-predicted a category by 30 percent for a month. Task: explain the miss and stop it recurring. Action: I compared forecast and actuals by region and found a new distribution channel had launched that the model knew nothing about. I told the planning team quickly, added channel as a feature and set up a weekly error review. Result: error returned to normal and planners trusted the alerts.
05
A product manager wants to test five different button designs at the same time. How do you set it up?
Run an A/B/n test with control plus five variants and equal random allocation. Calculate sample size for the smallest effect worth detecting, remembering that each variant gets only a sixth of traffic, so the test takes longer. Plan the analysis upfront: compare each variant to control and adjust for multiple comparisons. If the team has strong priors, test fewer designs first to save time.
06
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.
07
Tell me about a time you improved messy code written by someone else.
Situation: in my final-year project, a teammate's cleaning script was one 600-line notebook that took 20 minutes to run. Task: I needed to reuse it for new data. Action: I split it into small functions, added docstrings, replaced row loops with vectorised Pandas code and wrote a few checks for row counts. Result: run time fell to 2 minutes and the team could reuse the functions. I learned to refactor in small, tested steps.
Convert strings with pd.to_datetime, giving a format argument for speed and safety. Use the .dt accessor to pull year, month, weekday or hour. For time zones, first tz_localize naive timestamps to their real source zone, then tz_convert to a common zone such as UTC or Asia/Kolkata. Store data in UTC and convert only for reporting. Mixing naive and aware timestamps causes errors, so keep one convention across the pipeline.
09
Write Python code that validates prediction requests with Pydantic before passing them to a model.
U-Net is an encoder-decoder network. The encoder downsamples to capture context, and the decoder upsamples back to full resolution to produce a per-pixel mask. Skip connections copy encoder feature maps to the matching decoder level, so fine spatial detail lost during downsampling is recovered. It works well with limited labelled data, which made it the standard for medical imaging and industrial defect segmentation, and it is also the backbone of many diffusion models.
As the number of independent trials grows, the sample average converges to the true expected value, which is why larger samples give more stable estimates.
12
Write Pandas code to create lag and rolling features for each customer.
df = df.sort_values(['customer_id', 'month'])
g = df.groupby('customer_id')['spend']
df['spend_lag1'] = g.shift(1)
df['spend_lag3'] = g.shift(3)
# shift first so the current month is not included
df['spend_roll3_mean'] = g.transform(lambda s: s.shift(1).rolling(3).mean())
13
Write scikit-learn code to calibrate a gradient boosting classifier's probabilities and check the result.
Self-attention treats input tokens as a set, so without extra information it cannot tell word order: dog bites man and man bites dog look the same. Positional encoding adds position information to each token embedding. The original transformer used fixed sine and cosine patterns; BERT learned position embeddings; many modern LLMs use rotary embeddings, which encode relative positions and extend better to longer contexts.
The probability of seeing a result at least this extreme if the null hypothesis were true. A small p-value (for example < 0.05) is evidence against the null hypothesis.
19
What is the difference between an epoch, a batch and an iteration?
Stop or pause the affected part, fix the bug and restart the test with fresh data, since mixing broken and fixed periods biases results. Do not simply drop Android users from analysis afterwards, because that can break randomisation balance if the bug affected behaviour or logging. Document the incident, and add pre-launch checks such as running the variant through QA on every platform and monitoring guardrails in the first hours.
21
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.
22
What is the difference between additive and multiplicative seasonality?
With additive seasonality the seasonal swing is a fixed amount, for example weekends always add about 500 orders regardless of the overall level. With multiplicative seasonality the swing is a percentage of the level, so as the business grows the weekend spike grows too. A quick check is to plot the series: if the peaks widen as the trend rises, use multiplicative, or take a log and model it additively.
23
A deep network you built from scratch does not learn at all; the loss stays flat from the first epoch. Could weight initialisation be the reason?
If weights start too large, activations and gradients explode; if too small, they vanish, and training stalls. If all weights are equal, every neuron learns the same thing. Good schemes keep the variance of activations stable across layers: Xavier or Glorot initialisation for tanh and sigmoid, and He initialisation for ReLU. Frameworks use sensible defaults, but it matters when building custom layers or very deep networks.
24
Pandas: find customers whose monthly spend increased for three consecutive months.
Do not ship on conversion alone. Check whether the change shifted buyers to cheaper items, increased discount usage or pulled in low-value orders. Break down revenue into conversion times average order value, look at item mix, and confirm the revenue drop is statistically meaningful rather than noise. Then go back to the agreed primary metric and guardrails; if revenue is the business goal, it should guide the decision.
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.
The receptive field is the region of the input image that can influence one activation. Early layers see small patches like edges, and deeper layers see larger regions, enabling recognition of whole objects. It grows with kernel size, depth, stride and pooling, and dilated convolutions increase it without extra parameters. If the receptive field is smaller than the objects you care about, the network cannot capture their full context.
28
What is normalisation, and why are analytics tables often denormalised?
Normalisation splits data into related tables to remove redundancy, so each fact is stored once, which suits transactional systems with many updates. Analytics workloads mostly read large volumes, and joins across many tables are slow and error-prone. So warehouses often use denormalised, wide tables or star schemas that repeat some attributes. The trade-off is more storage and the need to keep copies consistent through pipelines.
A context manager sets something up and guarantees cleanup when a block ends, even if an error is raised. You use it with the with statement, for example with open('data.csv') as f, which always closes the file. It works through __enter__ and __exit__ methods, or the contextlib.contextmanager decorator. In data work it is used for files, database connections, locks and timers, so resources are never left open.
32
Write a basic PyTorch training loop for one epoch.
model.train()
for X, y in train_loader:
X, y = X.to(device), y.to(device)
optimizer.zero_grad()
logits = model(X)
loss = loss_fn(logits, y)
loss.backward()
optimizer.step()
# evaluation
model.eval()
with torch.no_grad():
val_loss = sum(loss_fn(model(Xv.to(device)), yv.to(device)).item() for Xv, yv in val_loader)
Use grid search, random search or Bayesian optimisation with cross-validation, starting with a coarse random search and narrowing the promising ranges.
34
What is the difference between apply, map and applymap in Pandas?
map works element-wise on a Series, apply works on a Series or along a DataFrame axis, and applymap (now DataFrame.map) applies a function to every cell.
35
What is chunking in RAG and how do you choose a chunk size?
Chunking splits documents into smaller pieces before embedding them, so retrieval returns focused passages that fit in the prompt. Small chunks, like 200 tokens, are precise but lose context; large chunks, like 1,000 tokens, keep context but add noise and cost. Split on natural boundaries such as headings and paragraphs, add some overlap, and keep metadata like title and section. Then tune chunk size using retrieval metrics on real questions.
36
What is the difference between a Pandas Series and a DataFrame?
A Series is a one-dimensional labelled array: one column of values with an index. A DataFrame is a two-dimensional table made of several Series that share the same index, and each column can have its own dtype. Selecting one column, like df['price'], gives you a Series. Selecting with a list, like df[['price']], gives you a DataFrame. Many methods work on both, but the output shape is different.
Which companies hire Data Science professionals in Jaipur?
Companies with large offices in Jaipur include Nagarro, Deutsche Bank. Use their company pages below to practise employer-specific sets.
Are Data Science interviews in Jaipur different from other cities?
The questions depend more on the company than the city. Jaipur's mix of employers decides the style: Many candidates here also apply to remote and hybrid roles with Delhi NCR teams
How do I find Data Science jobs in Jaipur?
Shortlist companies with Jaipur offices, keep your CV focused on Data Science projects, apply on company career pages and job portals with the Jaipur filter, and practise the questions on this page before each interview.