01
Explain logistic regression.
Show answer
It models the log-odds of a binary outcome as a linear function and passes it through a sigmoid to give probabilities, trained by maximising likelihood.
Quantiphi Data Science Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Quantiphi hiring for Data Science roles
Quantiphi is an AI-first digital engineering company that builds machine learning, data engineering, cloud and applied AI solutions for enterprises. It is known for close partnerships with major cloud providers and works with clients in healthcare, banking, media and retail. In India it hires machine learning engineers, data engineers, cloud engineers and software developers for global projects.
For Data Science roles, this Quantiphi practice set focuses most on Machine Learning (9), SQL for Data Science (5), Python for Data Science (4), Generative AI and LLMs (4). It has 4 easy, 30 medium, 6 hard questions, so you can start easy and work up to the harder ones.
- Company type
- Analytics
- Headquarters
- Marlborough, Massachusetts, USA
- Main India offices
- Mumbai, Bengaluru
Quantiphi Data Science interview process
- Online assessment (aptitude + coding + ML basics)
- Technical interview 1
- Technical interview 2
- HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
Solved for QuantiphiStart practicing
3 QUESTIONS FREESign up to unlock the full question bank and track your progress.Unlock every question and answer, then continue practising company-wise and package-wise.
3 questions free Unlock the rest in seconds
02
Why are NumPy arrays faster than Python lists?
Show answer
They store same-type values in contiguous memory and run vectorised operations in compiled C, avoiding Python loop overhead and per-object type checks.
03
Write SQL to find customers who ordered in January 2026 but not in February 2026.
Show answer
SELECT DISTINCT o.customer_id
FROM orders o
WHERE o.order_date >= '2026-01-01' AND o.order_date < '2026-02-01'
AND NOT EXISTS (
SELECT 1 FROM orders f
WHERE f.customer_id = o.customer_id
AND f.order_date >= '2026-02-01' AND f.order_date < '2026-03-01'
);
04
Your credit model has good overall AUC but performs much worse for applicants from one region. What do you do?
Show answer
First confirm with per-segment metrics and enough samples, since small groups are noisy. Then check why: fewer training examples from that region, different feature distributions, missing data or labels that reflect past biased decisions. Fixes include collecting more data, reweighting, segment-aware features or a separate calibration. Also review fairness and regulatory requirements, and agree with risk and compliance teams on acceptable performance gaps before launch.
05
Your LLM assistant confidently quotes a policy clause that does not exist. Why does this happen and how do you reduce it?
Show answer
Hallucination is when a model produces confident text that is false or not supported by any source. It happens because the model generates plausible tokens rather than checking facts. Reduce it by grounding answers in retrieved documents, asking the model to cite sources and say I do not know when context is missing, using low temperature, and validating outputs with rules or a second checking step. Measure it with a labelled evaluation set.
06
Explain exponential smoothing and the Holt-Winters method.
Show answer
Simple exponential smoothing forecasts with a weighted average of past values where weights decay exponentially, so recent points matter more. Holt's method adds a smoothed trend component. Holt-Winters adds a seasonal component as well, either additive or multiplicative. Each component has a smoothing parameter between 0 and 1 that is fitted to the data. These models are fast, easy to explain and strong baselines for retail and demand data.
07
The business asks you to pick a decision threshold for a churn model. How do you choose it?
Show answer
The default 0.5 is rarely right. I would get the cost of each outcome: the value of saving a churner, the cost of a retention offer, and the team's capacity to contact customers. Then I compute expected profit or precision and recall at many thresholds on validation data and pick the one that maximises value within capacity. I would also check that the chosen threshold stays stable over recent months.
08
When would you use MAE, MSE or RMSE for a regression model?
Show answer
MAE is the average absolute error; it is easy to explain and less sensitive to outliers. MSE squares errors, so large errors are punished heavily, and it is smooth for optimisation. RMSE is the square root of MSE, so it is in the same units as the target while still punishing big misses. Use RMSE when large errors are very costly, like stock-out forecasts, and MAE when outliers should not dominate.
09
What is MAPE and when is it a poor metric for forecast accuracy?
Show answer
MAPE is the mean of absolute errors divided by actual values, expressed as a percentage. It is easy to explain to business teams, but it breaks when actuals are zero or near zero, because the percentage explodes. It also penalises over-forecasts more than under-forecasts and weights small items heavily. For intermittent or low-volume series prefer MAE, weighted MAPE or MASE, which compares error to a naive baseline.
10
How do you evaluate clustering when you have no labels?
Show answer
Use internal metrics: the silhouette score compares how close a point is to its own cluster versus the nearest other cluster, and the Davies-Bouldin index measures cluster overlap. The elbow method looks at within-cluster variance. But the real test is usefulness: are clusters stable across random seeds and samples, can the business describe them, and do they behave differently on outcomes like spend or churn?
11
What is the attention mechanism?
Show answer
It lets a model weigh which input positions matter most for each output, computing weighted combinations of values from query-key similarity. It is the core of transformers.
12
Tell me about a time you deployed a model under a tight deadline.
Show answer
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.
13
You assign values to a filtered DataFrame and Pandas shows a SettingWithCopyWarning. What is going on and how do you fix it?
Show answer
The warning appears when you assign values to something that may be a copy of a slice instead of the original DataFrame, for example df[df.age > 30]['flag'] = 1. The original may not change. Fix it by using a single .loc call, like df.loc[df.age > 30, 'flag'] = 1, or by taking an explicit .copy() when you really want a separate DataFrame. Copy-on-write in newer Pandas versions makes this behaviour more predictable.
14
How do you design an A/B test?
Show answer
Define the hypothesis and one primary metric, calculate sample size from the baseline, minimum detectable effect, α and power, randomise users, run for full business cycles, check for sample-ratio mismatch, then test both statistical and practical significance.
15
After joining orders with a promotions table, total revenue doubled. What went wrong?
Show answer
The join created duplicate order rows, because some orders match more than one promotion row, so each amount is counted several times. Check by comparing COUNT(*) with COUNT(DISTINCT order_id) after the join. Fix it by aggregating the promotions table to one row per order first, picking one promotion with ROW_NUMBER, or summing revenue before the join. Always check row counts after every join.
16
Write Python code to find the top 3 most similar documents to a query using cosine similarity on embeddings.
Show answer
import numpy as np
def top_k(query_vec, doc_vecs, k=3):
q = query_vec / np.linalg.norm(query_vec)
d = doc_vecs / np.linalg.norm(doc_vecs, axis=1, keepdims=True)
scores = d @ q
idx = np.argsort(-scores)[:k]
return list(zip(idx, scores[idx]))
# doc_vecs shape: (n_docs, dim), query_vec shape: (dim,)
17
Your deployed model gives different predictions than the same model in your notebook. Why?
Show answer
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.
18
Pandas: find the top 3 products by revenue in each region.
Show answer
(df.assign(rev=df.price * df.qty)
.groupby(['region', 'product'])['rev'].sum()
.reset_index()
.sort_values('rev', ascending=False)
.groupby('region').head(3))
19
Write Python code to compute the haversine distance between customer and store locations.
Show answer
import numpy as np
def haversine_km(lat1, lon1, lat2, lon2):
lat1, lon1, lat2, lon2 = map(np.radians, [lat1, lon1, lat2, lon2])
dlat, dlon = lat2 - lat1, lon2 - lon1
a = np.sin(dlat / 2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2) ** 2
return 6371 * 2 * np.arcsin(np.sqrt(a))
df['dist_km'] = haversine_km(df.cust_lat, df.cust_lon, df.store_lat, df.store_lon)
20
Write Python code to calculate the sample size per group needed to detect a lift from 5 percent to 5.5 percent conversion.
Show answer
from statsmodels.stats.power import NormalIndPower
from statsmodels.stats.proportion import proportion_effectsize
baseline, target = 0.05, 0.055
effect = proportion_effectsize(target, baseline) # Cohen's h
n = NormalIndPower().solve_power(
effect_size=effect, alpha=0.05, power=0.8,
ratio=1.0, alternative='two-sided'
)
print(round(n)) # about 31,000 users per group
21
How do you handle personal data when sending customer text to an external LLM API?
Show answer
First check the data processing agreement, data residency and retention terms, and India's DPDP Act requirements with legal. Minimise what is sent: mask or tokenise names, phone numbers, Aadhaar and card numbers before the call, and restore them afterwards if needed. Use enterprise endpoints with no training on your data, log access, and consider a self-hosted open model for highly sensitive workloads.
22
How does Kubernetes help in serving machine learning models?
Show answer
Kubernetes runs containerised model services across a cluster. It restarts failed containers, scales the number of replicas based on traffic or CPU, rolls out new versions gradually and can roll back, and schedules GPU workloads on the right nodes. Tools built on it, like KServe or Seldon, add model-specific features such as canary releases and autoscaling to zero. The trade-off is operational complexity, so small teams often use managed services.
23
Write NumPy code to compute the Dice coefficient between a predicted and a true binary mask.
Show answer
import numpy as np
def dice(pred, true, eps=1e-7):
pred = pred.astype(bool)
true = true.astype(bool)
inter = np.logical_and(pred, true).sum()
return (2 * inter + eps) / (pred.sum() + true.sum() + eps)
# Dice = 2|A and B| / (|A| + |B|); 1 means perfect overlap
24
Your LLM app takes 8 seconds to respond and users complain. What do you do?
Show answer
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.
25
What is batch normalisation?
Show answer
It normalises layer inputs per mini-batch and then rescales them, stabilising and speeding up training and allowing higher learning rates.
26
What is SVM and the kernel trick?
Show answer
SVM finds the maximum-margin separating hyperplane. Kernels such as RBF compute similarity in a higher-dimensional space implicitly, so non-linear boundaries are possible.
27
Tell me about the most challenging machine learning project you have worked on.
Show answer
Situation: I built a lead scoring model where only 3 percent of leads converted and labels came 60 days late. Task: deliver a useful ranking for the sales team. Action: I set up a clean time-based validation, engineered activity features, used gradient boosting with class weights and measured lift in the top decile. Result: the top 10 percent of scored leads converted 3.5 times better than average. The hardest part was building trust in the labels.
28
What should you log for a production prediction service?
Show answer
For each request, log a request ID, timestamp, model version, input features or a hash of them, the prediction and score, and latency. Log errors and fallback use separately. Do not log raw personal data unless needed and allowed; mask it. Aggregate these logs into dashboards for traffic, latency percentiles, error rate and score distribution. Joining logged predictions with later outcomes gives the data for monitoring and retraining.
29
Your NLP classifier works well on English reviews but fails on Hinglish reviews. What do you do?
Show answer
First measure the gap with a labelled Hinglish test set. Hinglish mixes scripts and spellings, so tokenisers built for English split it badly. I would try a multilingual or Indic model such as mBERT, XLM-R or IndicBERT, normalise common spellings, and add labelled Hinglish data, possibly with transliteration. Evaluate per language segment and monitor the language mix in production, because it changes over time.
30
A key metric dropped 8 percent week over week. How do you check whether it is real?
Show answer
First rule out data issues: tracking changes, pipeline delays, missing partitions or a changed metric definition. Then compare with the same week last year and look for holidays or seasonality. Check normal weekly variation to see if 8 percent is unusual. Break the drop down by platform, region, app version and user type to find where it is concentrated. Finally, check releases, campaigns or outages that happened that week.
31
Write SQL to find duplicate emails in a users table.
Show answer
SELECT email, COUNT(*) AS cnt
FROM users
GROUP BY email
HAVING COUNT(*) > 1
ORDER BY cnt DESC;
32
How do you pull large data from a SQL database into Pandas efficiently?
Show answer
Push as much work as possible into SQL: filter, aggregate and select only needed columns before pulling. Use pd.read_sql with a SQLAlchemy engine and chunksize to stream large results. For very large extracts, export to Parquet from the warehouse and read that, which is much faster than row-by-row transfer. Set dtypes like category early, and never use SELECT star on a big table from a notebook.
33
What is the Global Interpreter Lock (GIL)?
Show answer
A mutex in CPython that lets only one thread execute Python bytecode at a time. Use multiprocessing for CPU-bound work and threads or asyncio for I/O-bound work.
34
Write Python code using Hugging Face transformers to run sentiment analysis on a list of reviews.
Show answer
from transformers import pipeline
clf = pipeline(
'sentiment-analysis',
model='distilbert-base-uncased-finetuned-sst-2-english'
)
reviews = ['Delivery was fast and the product is great',
'Packaging was damaged and support did not reply']
for text, out in zip(reviews, clf(reviews, batch_size=16)):
print(out['label'], round(out['score'], 3), '-', text)
35
How is an image represented for a deep learning model?
Show answer
An image is a numeric array of height by width by channels, where a colour image has three channels, usually RGB, with pixel values from 0 to 255. Libraries differ: OpenCV loads BGR in HWC order, while PyTorch expects tensors in CHW order with a batch dimension in front. Before training you convert to float, scale to 0-1 and normalise with the mean and standard deviation used by the pretrained model.
36
How does gradient boosting work step by step?
Show answer
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.
37
Why is XGBoost so popular?
Show answer
It combines gradient boosting with regularisation, handling of missing values, parallel tree construction and speed optimisations, and it performs strongly on tabular data.
38
Your daily sales forecast badly misses the Diwali spike every year. How do you fix it?
Show answer
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.
39
Write SQL to find the top 3 products by revenue in each category.
Show answer
WITH rev AS (
SELECT category, product_id, SUM(price * qty) AS revenue
FROM order_items
GROUP BY category, product_id
), ranked AS (
SELECT *, ROW_NUMBER() OVER (PARTITION BY category ORDER BY revenue DESC) AS rn
FROM rev
)
SELECT category, product_id, revenue
FROM ranked
WHERE rn <= 3;
40
What is sampling bias and how can you reduce it?
Show answer
Bias occurs when the sample does not represent the population. Reduce it with random or stratified sampling, higher response rates and by checking sample demographics against the population.
Tips to crack the Quantiphi Data Science interview
- Revise machine learning fundamentals such as regression, classification, evaluation metrics and overfitting, with clear intuitive explanations.
- Get hands-on with at least one cloud platform and mention any cloud certifications, since much client work runs on cloud services.
- Prepare a deep explanation of one ML or data project, including dataset, model choice, results and deployment approach.
- 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; Quantiphi interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Quantiphi Data Science interview?
Candidates usually go through 4 stages: Online assessment (aptitude + coding + ML basics); Technical interview 1; Technical interview 2; HR 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 Quantiphi Data Science interview?
This practice set leans on Machine Learning (9), SQL for Data Science (5), Python for Data Science (4), Generative AI and LLMs (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Quantiphi interviewers ask you to walk through real work.
Are these the exact questions asked at Quantiphi?
No. These are curated practice questions that match the skills and difficulty usually tested in analytics interviews like Quantiphi's. They are not a leaked or verified list of past questions.
Where does Quantiphi hire Data Science professionals in India?
Quantiphi has large teams in Mumbai, Bengaluru. Openings and locations change often, so check the Quantiphi careers page and filter by city and experience.
Data Science questions for similar companies
Analytics
Tiger Analytics
Data Science interview questions
Chennai, Bengaluru, Hyderabad
View questionsAnalytics
LatentView Analytics
Data Science interview questions
Chennai, Bengaluru
View questionsAnalytics
Affine
Data Science interview questions
Bengaluru
View questionsAnalytics
Fractal Analytics
Data Science interview questions
Mumbai, Bengaluru, Gurugram +1
View questionsAnalytics
Mu Sigma
Data Science interview questions
Bengaluru
View questionsAnalytics
Sigmoid
Data Science interview questions
Bengaluru, Pune
View questionsMore ways to practise
Company-wise Data Science questionsPackage-wise Data Science questionsExperience-wise Data Science questionsRole-wise Data Science questionsCity-wise Data Science questionsMost asked Data Science questionsScenario-based questionsPractical / hands-on questionsHR & behavioural questionsData Science mock interviewOnline assessment questionsTechnical round questionsManagerial round questionsData Science topic sheets