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Data Science Interview Questions for Telecom Companies

Q: Which telecom companies are covered?

This page links to 5 telecom companies, including Airtel, Jio Platforms, Ericsson, Nokia, Vodafone Idea. Each has its own page with rounds and a separate practice set.

Q: What rounds should I expect?

Common rounds at these companies include: HR round; Online assessment (aptitude + technical); Managerial round; Technical interview; Technical interview 1. The exact process varies by company and role.

40 questions weighted to the interview style of telecom companies — 10 easy, 29 medium, 1 hard.

How telecom companies interview for Data Science

Telecom and network companies handle huge volumes of customer and network data. Interviews focus on fundamentals, practical skills, and scenario questions about reliability, large datasets and customer-facing systems.

This set focuses on Deep Learning and NLP (10), Statistics and Probability (7), SQL for Data Science (4), Python for Data Science (4).

HR round Online assessment (aptitude + technical) Managerial round Technical interview Technical interview 1

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Write Python code to evaluate a forecasting model with expanding-window time series cross-validation.

Time Series & ForecastingMedium

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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)

Write a Python function to split a long text into overlapping chunks by word count for a RAG index.

Generative AI and LLMsMedium

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def chunk_words(text, size=200, overlap=40): if overlap >= size: raise ValueError('overlap must be smaller than size') words = text.split() chunks, step = [], size - overlap for start in range(0, len(words), step): chunks.append(' '.join(words[start:start + size])) if start + size >= len(words): break return chunks print(len(chunk_words(doc_text)))

What is effect size and why should you report it along with a p-value?

Statistics and ProbabilityMedium

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Effect size measures how large a difference is, for example the absolute lift in conversion or Cohen's d for means. A p-value only says whether the difference is likely due to chance. With millions of users, a 0.01 percent lift can be significant yet useless for the business. Reporting effect size with a confidence interval tells decision makers both whether the effect is real and whether it is worth acting on.

Your image classifier trained on 2,000 images memorises the training set within 5 epochs. What do you try?

Deep Learning and NLPMedium

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With so little data, I would use transfer learning from a pretrained model like ResNet or EfficientNet and fine-tune only the top layers first. I would add data augmentation such as flips, crops and colour changes, use dropout and weight decay, and apply early stopping on validation loss. Collecting more labelled images or using a smaller model also helps. I would check for duplicate images across train and validation sets too.

Write SQL to find the top 3 products by revenue in each category.

SQL for Data ScienceMedium

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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;

What is transfer learning?

Deep Learning and NLPMedium

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Reusing a model pretrained on a large dataset and fine-tuning it on a smaller task-specific dataset, saving data, time and compute.

What is tokenisation in NLP?

Deep Learning and NLPEasy

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Splitting text into units such as words, subwords or characters that are mapped to ids; subword methods like BPE handle rare words well.

Why are NumPy arrays faster than Python lists?

Python for Data ScienceMedium

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They store same-type values in contiguous memory and run vectorised operations in compiled C, avoiding Python loop overhead and per-object type checks.

What is the binomial distribution and when do you use it?

Statistics and ProbabilityEasy

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The binomial distribution gives the probability of getting k successes in n independent trials, each with the same success probability p. Its mean is n times p and its variance is n times p times (1 minus p). You use it for counts of yes-or-no outcomes, such as how many of 1,000 visitors convert or how many of 50 parts are defective. For large n it is well approximated by a normal distribution.

Write Pandas code to read a large CSV in chunks and compute total sales per city.

Python for Data ScienceMedium

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# 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)

What is a neural network?

Deep Learning and NLPEasy

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Layers of connected neurons that apply weights, biases and activation functions to learn complex patterns, trained by backpropagation.

What is backpropagation?

Deep Learning and NLPMedium

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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.

Your neural network's training loss suddenly becomes NaN. What do you check?

Deep Learning and NLPMedium

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Common causes are a learning rate that is too high, exploding gradients, bad input values, and unstable maths like log of zero. I would check the input data for NaN or infinite values, lower the learning rate, add gradient clipping, and make sure the loss uses the stable built-in version with logits. With mixed precision, I would check the loss scaler. Logging gradient norms per step helps find the exact point it breaks.

What is the difference between hard voting and soft voting in ensembles?

Machine LearningMedium

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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.

Your deployed model gives different predictions than the same model in your notebook. Why?

MLOps and Model DeploymentMedium

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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.

What is an isolation forest and how does it detect anomalies?

Machine LearningMedium

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An isolation forest builds many random trees that split data on random features at random thresholds. Anomalies are rare and different, so they get isolated in very few splits, giving a short average path length. Normal points need many splits. The anomaly score comes from that path length. It is fast, works without labels and scales to large data, but you must choose a contamination rate or score threshold carefully.

Tell me about a time you disagreed with a stakeholder about which metric to optimise.

MLOps and Model DeploymentMedium

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Situation: marketing wanted the lead model judged on overall accuracy. Task: agree on a metric that matched the goal. Action: I showed that accuracy rewarded predicting no conversion for everyone, then proposed precision in the top 1,000 leads per week, which matched the sales team's call capacity, with a simple chart. Result: they agreed, and the model with the better top-1,000 precision added 18 percent more conversions.

Write SQL to find customers who ordered in January 2026 but not in February 2026.

SQL for Data ScienceMedium

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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' );

What is conditional probability? Give an example.

Statistics and ProbabilityEasy

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Conditional probability is the chance of an event given that another event has happened, written P(A given B) = P(A and B) / P(B). Example: 10 percent of users buy, and 40 percent of users who add to cart buy. So P(buy given cart) is 0.4, much higher than the overall 0.1. It is the base of Bayes' theorem, Naive Bayes and many funnel analyses.

Tree feature importance ranks an ID-like numeric column near the top. Why, and which importance method would you trust instead?

Feature EngineeringHard

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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.

What is the difference between stemming and lemmatisation?

Deep Learning and NLPEasy

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Both reduce words to a base form. Stemming chops suffixes using rules, so studies becomes studi; it is fast but can produce non-words. Lemmatisation uses vocabulary and grammar to return the real dictionary form, so studies becomes study and better becomes good. Lemmatisation is more accurate but slower. With modern transformer models, neither is usually needed because subword tokenisers handle word forms.

What is a p-value?

Statistics and ProbabilityMedium

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The probability of seeing a result at least as extreme as the observed one if the null hypothesis were true. A small p-value is evidence against the null, not proof that the alternative is true.

What is the difference between ROW_NUMBER, RANK and DENSE_RANK?

SQL for Data ScienceEasy

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All three number rows within a window ordered by some column. ROW_NUMBER gives unique numbers even for ties: 1, 2, 3. RANK gives tied rows the same rank and skips the next numbers: 1, 1, 3. DENSE_RANK gives ties the same rank without gaps: 1, 1, 2. Use ROW_NUMBER to pick exactly one row per group, and DENSE_RANK for questions like the second highest distinct salary.

Your notebook runs on your laptop but fails on a colleague's machine. How do you make the work reproducible?

Python for Data ScienceMedium

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First pin the environment: a requirements.txt or conda environment file with exact versions, or a Docker image. Set random seeds for NumPy, scikit-learn and any framework you use. Remove hard-coded local paths and read them from a config file. Make sure data comes from a shared, versioned location. Finally, restart the kernel and run the notebook top to bottom, so hidden cell order bugs are caught before sharing.

Training a transformer on a GPU fails with an out-of-memory error. What can you do?

Deep Learning and NLPMedium

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Reduce the batch size and use gradient accumulation to keep the same effective batch. Shorten the maximum sequence length. Turn on mixed precision training with float16 or bfloat16. Use gradient checkpointing, which saves memory by recomputing activations. Freeze some layers or switch to a smaller model, or use parameter-efficient fine-tuning like LoRA. Also make sure no old tensors stay on the GPU between runs.

What is a baseline model and why should you build one first?

Machine LearningEasy

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A baseline is the simplest reasonable prediction, such as predicting the most common class, last week's value or a basic logistic regression. It sets the minimum score any real model must beat. Without it, a 75 percent accuracy can look good when predicting the majority class already gives 80 percent. Baselines also catch pipeline bugs early and show whether extra complexity is worth it.

What is sampling bias and how can you reduce it?

Statistics and ProbabilityMedium

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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.

Type I vs Type II error?

Statistics and ProbabilityEasy

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Type I = false positive (rejecting a true null), probability α. Type II = false negative (missing a real effect), probability β. Power = 1 − β.

Write Pandas code to create lag and rolling features for each customer.

Feature EngineeringMedium

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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())

What is subword tokenisation like BPE or WordPiece?

Deep Learning and NLPMedium

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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.

Write a function to calculate RMSE without any library.

Python for Data ScienceEasy

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def rmse(y, p): return (sum((a - b) ** 2 for a, b in zip(y, p)) / len(y)) ** 0.5

Explain Bayes' theorem with an example.

Statistics and ProbabilityMedium

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P(A given B) = P(B given A) x P(A) / P(B). In a medical test it converts test accuracy and disease prevalence into the real chance a positive result means disease.

Your product image classifier works on studio catalogue photos but fails on photos users take with their phones. Why, and what do you do?

Computer VisionMedium

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This is domain shift: user photos have clutter, blur, poor lighting and odd angles that the model never saw. Collect and label a sample of real user photos and evaluate on them separately. Add augmentation for blur, noise, colour jitter, rotation and random crops, and fine-tune on a mix of studio and real images. Monitor accuracy on user photos in production, since that is the distribution that matters.

What is chunking in RAG and how do you choose a chunk size?

Generative AI and LLMsMedium

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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.

What is the difference between a star schema and a snowflake schema?

SQL for Data ScienceMedium

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A star schema has a central fact table, like sales with amounts and foreign keys, surrounded by denormalised dimension tables such as product, customer and date. A snowflake schema normalises dimensions further, for example product links to a separate category table. Star schemas need fewer joins and are faster and simpler for analysts. Snowflake schemas save storage and reduce duplication but make queries more complex.

Tell me about a time you deployed a model under a tight deadline.

MLOps and Model DeploymentMedium

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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.

Tell me about a time you had to learn a new deep learning framework or tool quickly.

Deep Learning and NLPMedium

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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.

How do you work with domain experts to find good features?

Feature EngineeringMedium

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Situation: on an insurance claims model, my first features gave average results. Task: improve fraud detection. Action: I sat with two claim investigators for a day and asked what made them suspicious. They mentioned claims filed soon after policy start and repeated garage names. I turned these into features and checked them with the team. Result: recall at the same precision rose by 12 points. Experts often know signals the data hides.

Write Python code to compare revenue per user between two groups using Welch's t-test.

A/B Testing & ExperimentationMedium

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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}')

Write Pandas code to resample daily sales into weekly totals and monthly averages.

Time Series & ForecastingEasy

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import pandas as pd df['date'] = pd.to_datetime(df['date']) s = df.set_index('date')['sales'].sort_index() # Weekly totals, weeks ending on Sunday weekly = s.resample('W-SUN').sum() # Monthly average of daily sales monthly = s.resample('MS').mean() print(weekly.head()) print(monthly.head())