01
How does a CNN work?
Show answer
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.
Ola Data Science Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Ola hiring for Data Science roles
Ola, operated by ANI Technologies, is an Indian ride-hailing platform offering cabs, auto-rickshaws and bike taxis, and it is part of a group that also includes Ola Electric and Krutrim. Its technology and analytics teams work on pricing, dispatch, maps and driver supply. It hires for software engineers, SDETs, business analysts and data scientists.
For Data Science roles, this Ola practice set focuses most on Computer Vision (6), Deep Learning and NLP (5), Machine Learning (5), MLOps and Model Deployment (5). It has 27 medium, 13 hard questions, so you can start easy and work up to the harder ones.
- Company type
- Product
- Headquarters
- Bengaluru, India
- Main India offices
- Bengaluru
Ola Data Science interview process
- Online coding assessment
- Technical round 1 (DSA)
- Technical round 2 (design or case)
- Hiring manager round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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02
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
03
You added an index but the query is still slow. How do indexes work, and when do they fail to help or even hurt?
Show answer
An index is a separate sorted structure, usually a B-tree, that lets the database find rows without scanning the whole table, like a book index. It speeds up filters, joins and sorting on the indexed columns. Indexes cost storage and slow down inserts, updates and deletes because each index must be updated. They may be ignored for low-selectivity columns or when a function is applied to the column in WHERE.
04
A query on a 200 million row table takes 20 minutes. How do you speed it up?
Show answer
Start with EXPLAIN to see the plan and find full scans, bad joins or spills. Filter as early as possible and select only needed columns. Make sure the table is partitioned or clustered on the date column and that the query filters on it. Check join keys for type mismatches and duplicates that explode rows. Pre-aggregate into a summary table if the query runs daily, and replace correlated subqueries with joins or window functions.
05
A feature was launched to all users with no control group. How would you estimate its causal impact?
Show answer
Without randomisation I would use quasi-experimental methods. Difference-in-differences compares the change in a group that got the feature early with one that did not, if rollout was staggered. Interrupted time series or synthetic control builds a counterfactual from pre-launch trends or similar markets. Regression discontinuity works if eligibility had a cut-off. I would state the assumptions clearly and check them with placebo tests.
06
Write Python code to evaluate a forecasting model with expanding-window time series cross-validation.
Show answer
import numpy as np
from sklearn.model_selection import TimeSeriesSplit
from sklearn.metrics import mean_absolute_error
tscv = TimeSeriesSplit(n_splits=5, test_size=28)
scores = []
for tr, te in tscv.split(X):
model.fit(X.iloc[tr], y.iloc[tr])
pred = model.predict(X.iloc[te])
scores.append(mean_absolute_error(y.iloc[te], pred))
print(np.mean(scores), scores)
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
Write Python code that validates prediction requests with Pydantic before passing them to a model.
Show answer
from pydantic import BaseModel, Field, ValidationError
class LoanRequest(BaseModel):
age: int = Field(ge=18, le=100)
income: float = Field(gt=0)
city: str = Field(min_length=2)
def predict(payload: dict):
try:
req = LoanRequest(**payload)
except ValidationError as e:
return {'error': e.errors()}
return {'score': float(model.predict_proba([[req.age, req.income]])[0, 1])}
09
Your classifier scores 98 percent on validation, but Grad-CAM shows it focuses on a watermark rather than the object. What is happening?
Show answer
The model has learned a shortcut: the watermark correlates with the label in your dataset, perhaps because images of one class came from a single source. It will fail when that correlation breaks. Remove or crop the watermarks, rebalance sources across classes, and create a validation set from different sources. Keep using saliency maps and slice-based evaluation to catch other spurious cues such as backgrounds or text.
10
Write PyTorch code to fine-tune a pretrained ResNet-18 for a 5-class problem by replacing its final layer.
Show answer
import torch, torch.nn as nn
from torchvision import models
model = models.resnet18(weights=models.ResNet18_Weights.DEFAULT)
for p in model.parameters():
p.requires_grad = False # freeze backbone
model.fc = nn.Linear(model.fc.in_features, 5)
opt = torch.optim.AdamW(model.fc.parameters(), lr=1e-3)
loss_fn = nn.CrossEntropyLoss()
# train head first, then unfreeze later layers with a lower lr
11
Tree feature importance ranks an ID-like numeric column near the top. Why, and which importance method would you trust instead?
Show answer
Impurity-based importance, the default in tree models, sums how much each feature reduces impurity across splits. It is fast but biased toward high-cardinality and continuous features, and it is computed on training data. Permutation importance shuffles one feature at a time on validation data and measures the drop in score, so it reflects real predictive value. Both can understate correlated features, because the model can lean on the copy.
12
What are hybrid search and reranking in retrieval systems?
Show answer
Hybrid search combines keyword search, like BM25, with vector search. Keywords catch exact matches such as product codes and names, while vectors catch meaning and paraphrases. Results are merged, often with reciprocal rank fusion. Reranking then takes the top 20 to 50 candidates and scores each one against the query with a cross-encoder model, which is slower but more accurate. Together they usually improve RAG answer quality noticeably.
13
You need forecasts for 50,000 SKUs every week. Would you build one model per series or one global model?
Show answer
One classical model per SKU is simple and interpretable but expensive to maintain, and it fails for short or sparse series. A global model, such as LightGBM trained on all SKUs with lags, calendar and product features, learns shared patterns and handles new items better. In practice I would use a global model as the main engine, keep statistical models for a few high-value series, and compare both in a rolling backtest.
14
Why is cross-entropy preferred over MSE for classification in neural networks?
Show answer
Cross-entropy measures how far predicted probabilities are from the true class and strongly penalises confident wrong predictions. With a softmax or sigmoid output, it gives large, useful gradients when the model is wrong. MSE with sigmoid outputs gives very small gradients when predictions saturate, so learning slows down. Use binary cross-entropy for two classes, categorical cross-entropy for many classes, and MSE for regression targets.
15
Write a prompt template for classifying customer support tickets.
Show answer
System: You classify support tickets for an e-commerce company. Categories: payment, delivery, refund, account, other. Rules: choose exactly one category; if unsure, choose other; do not invent details. Return JSON with fields category and reason, reason under 20 words. Then two short examples showing a ticket and the expected JSON. User: Ticket: {ticket_text}. Keep the template fixed and test it on 100 labelled tickets before rollout.
16
Tell me about a time you stopped a team from shipping a change based on a flawed experiment.
Show answer
Situation: a team wanted to launch a feature after a test showed a 6 percent lift. Task: as the reviewing data scientist, validate it. Action: I found that assignment was triggered after users clicked into the feature, so treatment had only engaged users. I explained the selection bias simply, proposed assigning at app open, and helped rerun it. Result: the true lift was under 1 percent, and we added assignment checks to the review template.
17
What is hierarchical forecasting and why is reconciliation needed?
Show answer
Hierarchical forecasting produces forecasts at several levels, such as SKU, store, city and national totals. If you forecast each level independently, the store forecasts will not add up to the city forecast. Reconciliation adjusts them to be consistent. Bottom-up sums the lowest level, top-down splits the total by proportions, and methods like MinT combine all levels optimally. Consistent numbers matter because finance, supply chain and stores plan from different levels.
18
Write scikit-learn code to get precision, recall and a confusion matrix at a custom threshold of 0.3.
Show answer
from sklearn.metrics import confusion_matrix, precision_score, recall_score
proba = model.predict_proba(X_test)[:, 1]
pred = (proba >= 0.3).astype(int)
print(confusion_matrix(y_test, pred))
print('precision', precision_score(y_test, pred))
print('recall', recall_score(y_test, pred))
19
What is subword tokenisation like BPE or WordPiece?
Show answer
Subword tokenisers split text into frequent pieces between characters and full words. Byte Pair Encoding starts from characters and repeatedly merges the most frequent pairs. WordPiece, used by BERT, is similar but chooses merges by likelihood. Rare words like unhappiness become un, happi, ness, so there are no unknown words and the vocabulary stays around 30,000 to 100,000. Token counts also decide LLM cost and context use.
20
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.
21
Your GPU model-serving bill has doubled while traffic grew only 20 percent. How do you bring cost down?
Show answer
Profile utilisation first; idle or under-filled GPUs are common. Enable dynamic batching so each GPU call handles several requests, right-size instance types and autoscale on queue depth rather than CPU. Reduce model cost with quantisation, distillation or a smaller model for easy requests. Cache repeated predictions, move non-urgent work to batch jobs on cheaper hardware, and track cost per thousand predictions as a standing metric.
22
What is an autoencoder and what is it used for?
Show answer
A network that compresses inputs into a small latent code and reconstructs them; used for denoising, dimensionality reduction and anomaly detection.
23
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;
24
How do you evaluate and explain a model to business stakeholders?
Show answer
Tie metrics to business outcomes such as cost saved, show feature importance or SHAP values, give example predictions and be transparent about limitations.
25
Soon after a new model version went live, customer complaints spiked. How do you roll back safely and learn from it?
Show answer
Roll back immediately to the previous registry version through the deployment pipeline, rather than editing servers by hand, and confirm metrics recover. Then investigate offline: compare predictions of both versions on the same traffic, check feature pipelines and data changes, and review what the offline evaluation missed. Add the failing cases to the test set, and use canary or shadow rollout with alert thresholds for future releases.
26
How do you turn text columns into features for a tabular model?
Show answer
Simple options are length, word count and keyword flags. TF-IDF vectors capture important words and work well with linear models. For meaning, use sentence embeddings from a pretrained model and optionally reduce them with PCA. You can also train a separate text classifier and feed its predicted score as one feature. Fit any vocabulary or encoder on training data only to avoid leakage.
27
What is the difference between hard voting and soft voting in ensembles?
Show answer
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.
28
Write Python code to compare revenue per user between two groups using Welch's t-test.
Show answer
import numpy as np
from scipy import stats
ctrl = df.loc[df.variant == 'control', 'revenue'].to_numpy()
treat = df.loc[df.variant == 'treatment', 'revenue'].to_numpy()
t, p = stats.ttest_ind(treat, ctrl, equal_var=False) # Welch
diff = treat.mean() - ctrl.mean()
se = np.sqrt(treat.var(ddof=1) / len(treat) + ctrl.var(ddof=1) / len(ctrl))
print(f'diff={diff:.2f} 95% CI=({diff - 1.96*se:.2f}, {diff + 1.96*se:.2f}) p={p:.4f}')
29
What is a confidence interval?
Show answer
A range built from sample data that would contain the true parameter in a stated percentage (for example 95%) of repeated samples. It is not the probability that the parameter lies inside this one interval.
30
What is SHAP and how does it explain model predictions?
Show answer
SHAP assigns each feature a contribution to a single prediction based on Shapley values from game theory. The contributions add up to the difference between the prediction and the average prediction. It gives local explanations, like why one loan was rejected, and global views by aggregating values across rows. TreeSHAP is fast for tree models. Limits include slow computation for other models and misleading results when features are strongly correlated.
31
What are embeddings and vector databases, and why do LLM apps use them?
Show answer
An embedding model turns text into a vector so that similar meanings are close in space. A vector database stores these vectors with metadata and finds the nearest ones to a query vector quickly using approximate nearest neighbour indexes like HNSW. LLM apps use them for semantic search and retrieval-augmented generation, finding the most relevant documents to put into the prompt. Examples include pgvector, FAISS and dedicated vector stores.
32
Your test shows a 2 percent lift in conversion, but revenue per user went down. What do you do?
Show answer
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.
33
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.
34
Write Pandas code to read a large CSV in chunks and compute total sales per city.
Show answer
# process 1 million rows at a time
import pandas as pd
totals = {}
for chunk in pd.read_csv('sales.csv', usecols=['city', 'amount'], chunksize=1_000_000):
part = chunk.groupby('city')['amount'].sum()
for city, amt in part.items():
totals[city] = totals.get(city, 0) + amt
result = pd.Series(totals).sort_values(ascending=False)
35
Train accuracy is 95% but test accuracy is 70%. What do you do?
Show answer
This is overfitting. Check for data leakage, simplify the model, add regularization, get more data or augment it, use cross-validation, early stopping and feature selection.
36
Write NumPy code for non-maximum suppression on a set of boxes and scores.
Show answer
import numpy as np
def nms(boxes, scores, thr=0.5):
x1, y1, x2, y2 = boxes.T
areas = (x2 - x1) * (y2 - y1)
order = scores.argsort()[::-1]
keep = []
while order.size:
i = order[0]
keep.append(i)
w = np.maximum(0, np.minimum(x2[i], x2[order[1:]]) - np.maximum(x1[i], x1[order[1:]]))
h = np.maximum(0, np.minimum(y2[i], y2[order[1:]]) - np.maximum(y1[i], y1[order[1:]]))
inter = w * h
iou = inter / (areas[i] + areas[order[1:]] - inter)
order = order[1:][iou < thr]
return keep
37
Your SQL result does not match the number on the official dashboard. How do you resolve it?
Show answer
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.
38
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.
39
What is the difference between semantic, instance and panoptic segmentation?
Show answer
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.
40
Write a Python function to compute IoU between two bounding boxes given as (x1, y1, x2, y2).
Show answer
def iou(a, b):
ix1, iy1 = max(a[0], b[0]), max(a[1], b[1])
ix2, iy2 = min(a[2], b[2]), min(a[3], b[3])
inter = max(0, ix2 - ix1) * max(0, iy2 - iy1)
area_a = (a[2] - a[0]) * (a[3] - a[1])
area_b = (b[2] - b[0]) * (b[3] - b[1])
union = area_a + area_b - inter
return inter / union if union > 0 else 0.0
print(iou((0, 0, 10, 10), (5, 5, 15, 15))) # 0.142...
Tips to crack the Ola Data Science interview
- Prepare marketplace cases on driver supply, rider demand and surge pricing in Indian cities.
- Practise DSA problems on graphs and heaps, which relate to dispatch and route optimisation.
- Read about Ola Maps and Krutrim AI efforts, which are recent technology focus areas for the group.
- 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; Ola interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Ola Data Science interview?
Candidates usually go through 4 stages: Online coding assessment; Technical round 1 (DSA); Technical round 2 (design or case); 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 Ola Data Science interview?
This practice set leans on Computer Vision (6), Deep Learning and NLP (5), Machine Learning (5), MLOps and Model Deployment (5). Revise those first, then practise explaining one or two of your own projects end to end, because most Ola interviewers ask you to walk through real work.
Are these the exact questions asked at Ola?
No. These are curated practice questions that match the skills and difficulty usually tested in product interviews like Ola's. They are not a leaked or verified list of past questions.
Where does Ola hire Data Science professionals in India?
Ola has large teams in Bengaluru. Openings and locations change often, so check the Ola careers page and filter by city and experience.
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