Samsung Data Science Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Samsung hiring for Data Science roles
Samsung Electronics makes smartphones, consumer electronics, semiconductors and home appliances. Its India R&D institutes in Bengaluru, Noida and Delhi work on mobile software, AI, camera, connectivity and data. It hires for software engineers, test engineers and data professionals, with campus hiring known for coding-focused tests.
For Data Science roles, this Samsung practice set focuses most on MLOps and Model Deployment (8), Statistics and Probability (7), Deep Learning and NLP (5), Machine Learning (5). It has 4 easy, 26 medium, 10 hard questions, so you can start easy and work up to the harder ones.
Company type
Hardware & semiconductors
Headquarters
Suwon, South Korea
Main India offices
Bengaluru, Noida, Delhi
Samsung Data Science interview process
Online coding test (Samsung competency test)
Technical interview 1
Technical interview 2
HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
Solved for SamsungStart practicing
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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.
04
Write a scikit-learn ColumnTransformer that preprocesses numeric and categorical columns.
The category dtype stores each unique value once and keeps small integer codes for every row. For a column like city with 50 unique values in 10 million rows, this can cut memory by more than 90 percent and speed up groupby and sorting. It helps when a column has low cardinality. It does not help for unique IDs or free text, and adding new categories later needs extra handling.
06
How does Kubernetes help in serving machine learning models?
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.
07
Write Python code to compute the Population Stability Index for a feature to detect drift.
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.
09
What is the binomial distribution and when do you use it?
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.
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.
11
How do you deploy and monitor a machine learning model?
Package the model behind an API with versioning, track latency and errors, monitor input drift and prediction quality, and automate retraining with rollback.
12
Tell me about a time you disagreed with a stakeholder about which metric to optimise.
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.
13
What does the axis parameter mean in NumPy and Pandas?
axis tells an operation which direction to work along. axis=0 works down the rows, so df.sum(axis=0) gives one total per column. axis=1 works across columns, so df.sum(axis=1) gives one total per row. In df.drop, axis=1 means drop columns and axis=0 means drop rows. A simple way to remember it: the axis you name is the one that gets collapsed or removed.
14
What is subword tokenisation like BPE or WordPiece?
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.
The ROC curve plots true positive rate against false positive rate across thresholds. AUC is the probability that a random positive is ranked above a random negative; 0.5 is chance.
Bootstrapping estimates the uncertainty of a statistic by resampling your data with replacement many times, computing the statistic on each resample, and looking at the spread of results. It gives confidence intervals for things with no simple formula, like the median, a ratio or a model metric such as AUC. It needs no normality assumption, but it relies on the sample being representative and can be slow on very large data.
17
You added an index but the query is still slow. How do indexes work, and when do they fail to help or even hurt?
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.
18
Your LLM feature costs far more than planned. How do you reduce cost without hurting quality?
Measure cost per request and find the biggest token users. Shorten system prompts and retrieved context, and cap output length. Route simple requests to a smaller, cheaper model and keep the large model for hard ones. Cache repeated answers and use provider prompt caching for fixed prefixes. Batch offline jobs. Consider fine-tuning a small model for a high-volume narrow task. Check every change against the evaluation set.
19
How would you design an ML platform for a team of 20 data scientists?
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.
20
Your credit model has good overall AUC but performs much worse for applicants from one region. What do you do?
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.
21
What is the difference between an epoch, a batch and an iteration?
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.
24
Halfway through a test you discover a bug that broke the treatment for Android users. What do you do?
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.
25
What is SARIMA and how does it extend a plain ARIMA model?
SARIMA adds seasonal terms to ARIMA. It is written ARIMA(p,d,q)(P,D,Q)m, where the second set are seasonal AR, differencing and MA orders, and m is the season length, such as 7 for daily data with a weekly cycle or 12 for monthly data. SARIMAX further allows external regressors like price or holidays. It handles one seasonal period well but struggles with multiple seasonalities, such as daily plus yearly.
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.
27
Write SQL to pivot order counts by status into columns using CASE WHEN.
SELECT DATE_TRUNC('month', order_date) AS month,
SUM(CASE WHEN status = 'delivered' THEN 1 ELSE 0 END) AS delivered,
SUM(CASE WHEN status = 'cancelled' THEN 1 ELSE 0 END) AS cancelled,
SUM(CASE WHEN status = 'returned' THEN 1 ELSE 0 END) AS returned
FROM orders
GROUP BY 1
ORDER BY 1;
28
What are hybrid search and reranking in retrieval systems?
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.
Quantisation stores model weights in lower precision, such as 8-bit or 4-bit integers instead of 16-bit floats. A 7-billion parameter model drops from about 14 GB to around 4 GB at 4-bit, so it fits on smaller GPUs or even laptops and runs faster. The cost is some loss in quality, usually small at 8-bit and noticeable on hard tasks at very low bits. Common formats include GPTQ, AWQ and GGUF.
30
Why do residual connections make very deep networks easier to train?
A residual block adds its input to its output, so the layers learn a residual F(x) and the block outputs F(x) plus x. If extra layers are not useful, they can learn to output near zero and the block behaves like identity. The skip path also lets gradients flow directly to early layers, reducing vanishing gradients. This is why ResNets with 50 or 100-plus layers train well where plain deep networks degrade.
Function calling lets an LLM respond with a structured request to call a tool you defined, such as get_order_status with an order_id, instead of plain text. Your code runs the function, returns the result to the model, and the model uses it in its final answer. This connects LLMs to live data, calculators and APIs. You must validate arguments, limit which tools exist and handle errors, because the model can choose wrong inputs.
32
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.
33
Write Keras code for a small binary classifier on tabular data.
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.
35
Write Python code to calculate the sample size per group needed to detect a lift from 5 percent to 5.5 percent conversion.
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.
37
A feature was launched to all users with no control group. How would you estimate its causal impact?
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.
ANOVA, analysis of variance, tests whether three or more group means are equal. It compares variation between groups to variation within groups using an F statistic. For example, comparing average order value across four city tiers. A significant result says at least one mean differs but not which one, so you follow up with post-hoc tests like Tukey's HSD. It assumes independent samples, roughly normal residuals and similar variances.
39
How do you evaluate clustering when you have no labels?
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?
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.
Tips to crack the Samsung Data Science interview
Practise Samsung-style coding problems based on BFS, DFS, backtracking and simulation without using built-in libraries.
Revise C, C++ or Java fundamentals deeply along with operating systems and data structures.
Read about Samsung R&D work in India on Galaxy devices, One UI and on-device AI.
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; Samsung interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Samsung Data Science interview?
Candidates usually go through 4 stages: Online coding test (Samsung competency test); 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 Samsung Data Science interview?
This practice set leans on MLOps and Model Deployment (8), Statistics and Probability (7), Deep Learning and NLP (5), Machine Learning (5). Revise those first, then practise explaining one or two of your own projects end to end, because most Samsung interviewers ask you to walk through real work.
Are these the exact questions asked at Samsung?
No. These are curated practice questions that match the skills and difficulty usually tested in hardware & semiconductors interviews like Samsung's. They are not a leaked or verified list of past questions.
Where does Samsung hire Data Science professionals in India?
Samsung has large teams in Bengaluru, Noida, Delhi. Openings and locations change often, so check the Samsung careers page and filter by city and experience.