GlobalLogic Data Science Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About GlobalLogic hiring for Data Science roles
GlobalLogic, a Hitachi Group company, is a digital engineering services firm working on product development, embedded software, cloud and data for clients in automotive, healthcare, media and telecom. Its India engineering centres are among its largest. It hires for software engineers, test engineers, data engineers and embedded developers.
For Data Science roles, this GlobalLogic practice set focuses most on Python for Data Science (9), Machine Learning (8), Deep Learning and NLP (6), SQL for Data Science (4). It has 18 easy, 20 medium, 2 hard questions, so you can start easy and work up to the harder ones.
Company type
IT services
Headquarters
San Jose, California, USA
Main India offices
Noida, Bengaluru, Gurugram, Hyderabad, Nagpur
GlobalLogic Data Science interview process
Online assessment (aptitude + coding)
Technical round 1
Technical round 2
HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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01
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.
02
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.
03
What is standard deviation and what does it tell you?
Split the data into k folds, train on k−1 folds and validate on the remaining one, rotate and average the scores. It gives a more reliable estimate than one split and helps tune hyperparameters without touching the test set.
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.
06
Your sales forecast scores well in k-fold cross-validation but fails in production. What could be wrong with the validation?
Random k-fold mixes future and past rows, so the model trains on data from after the period it predicts. That leaks information and inflates scores. Use time-based splits instead: train on an expanding or rolling window and validate on the next period, like TimeSeriesSplit in scikit-learn. Leave a gap between train and validation when features use lagged values, so the test reflects real forecasting conditions.
Situation: leadership believed a discount campaign drove a sales spike. Task: I was asked to measure its impact. Action: I compared similar regions with and without the campaign and found the spike matched a festival week in both. I shared the method, the charts and the limits openly. Result: they cut the next discount budget by half and moved it to retention. I learned to present surprising results with clear evidence and calm framing.
09
Write Pandas code to resample daily sales into weekly totals and monthly averages.
*args collects extra positional arguments into a tuple; **kwargs collects extra keyword arguments into a dict. They make function signatures flexible and are common in wrappers and decorators.
11
What is the difference between SGD and Adam optimisers?
SGD updates weights using the gradient times a fixed learning rate, often with momentum. Adam keeps running averages of gradients and their squares, giving each parameter its own adaptive step size. Adam converges fast with little tuning and is the default for transformers. Well-tuned SGD with momentum sometimes generalises better on vision tasks. AdamW, which decouples weight decay, is the common choice for modern deep learning.
They build a list in one readable expression, for example [x*x for x in data if x > 0]. They are usually faster than an equivalent for-loop with append.
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.
14
Your SQL result does not match the number on the official dashboard. How do you resolve it?
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.
15
Why do we use activation functions? Why is ReLU popular?
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.
17
What are trend, seasonality and noise in a time series?
Trend is the long-term direction of the series, such as steady growth in monthly users. Seasonality is a pattern that repeats at a fixed period, like higher sales every weekend or every festive season. Noise is the random variation left after removing trend and seasonality. Separating these parts, through decomposition, helps you understand the data and pick a model that can capture each component properly.
18
Tell me about a feature you engineered that significantly improved a model.
Situation: our delivery time model kept underestimating late-night orders. Task: reduce large errors. Action: I noticed restaurant prep time depended on how busy the kitchen was, so I built a feature for active orders at that restaurant in the last 30 minutes, computed point in time. Result: MAE dropped by 14 percent and late-night errors fell the most. It taught me to study the worst errors before adding generic features.
19
When would you use MAE, MSE or RMSE for a regression model?
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.
20
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'))
A DAG, directed acyclic graph, in Apache Airflow defines a workflow as tasks with dependencies, such as extract data, build features, train model and publish scores, in that order. Airflow schedules runs, retries failed tasks, passes run dates and shows status in a web UI. Acyclic means no task can loop back to an earlier one. Data scientists use it for scheduled training and batch scoring pipelines.
22
How do you pull large data from a SQL database into Pandas efficiently?
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.
23
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.
24
Pandas: convert wide monthly columns into a long format table.
SELECT user_id, order_date, amount,
SUM(amount) OVER (
PARTITION BY user_id
ORDER BY order_date
ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
) AS running_spend
FROM orders
ORDER BY user_id, order_date;
26
Write SQL to show each customer's first order date, last order date and the gap between them in days.
SELECT
customer_id,
MIN(order_date) AS first_order,
MAX(order_date) AS last_order,
MAX(order_date) - MIN(order_date) AS days_between,
COUNT(*) AS orders
FROM orders
GROUP BY customer_id
ORDER BY days_between DESC;
-- PostgreSQL: DATE minus DATE gives days; use DATEDIFF in other engines
27
How do you use groupby with multiple aggregations?
A time series is a sequence of observations recorded at ordered time steps, such as daily sales or hourly website traffic. Unlike ordinary tabular data, rows are not independent: today's value depends on yesterday's, and the order cannot be shuffled. This changes everything downstream. You must split train and test by time, build features only from the past, and check for trend and seasonality before choosing a model.
29
How does K-means clustering work and how do you choose k?
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.
Recursive feature elimination trains a model, ranks features by importance or coefficient size, removes the weakest ones, and repeats until the desired number remains. RFECV picks the number of features using cross-validation. It considers features together rather than one at a time. The downsides are high compute cost on large data and unstable results when features are correlated, so check stability across folds.
32
Tell me about a GenAI project you built and what you learned from it.
Situation: our HR team answered the same leave and policy questions daily. Task: build an internal assistant in six weeks. Action: I built a RAG pipeline over policy PDFs, added citations, created a 150-question evaluation set with HR, and iterated on chunking and reranking. Result: answer accuracy reached 91 percent and HR queries dropped by 40 percent. I learned that the evaluation set mattered more than the choice of model.
Use filter methods (correlation, chi-square), wrapper methods (RFE), embedded methods (Lasso, tree importance) and domain knowledge, validating each choice with cross-validation.
34
What is the difference between bagging and boosting?
Bagging trains models in parallel on bootstrap samples and averages them (reduces variance, e.g. Random Forest). Boosting trains models one after another, each fixing the previous errors (reduces bias, e.g. XGBoost).
Data augmentation creates new training examples by applying realistic changes to existing ones. For images this means flips, rotations, crops and brightness changes. For text it can mean synonym replacement, back-translation or random word dropping. For audio, adding noise or shifting pitch. It improves generalisation when data is limited. Changes must keep the label correct; flipping a digit 6 can turn it into a 9.
36
How do you choose a learning rate for a neural network?
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.
38
What is the minimum detectable effect (MDE) in experiment design?
The MDE is the smallest true effect your test can reliably detect given the sample size, baseline rate, significance level and power. If baseline conversion is 5 percent and your MDE is 10 percent relative, you can detect a move to 5.5 percent but will likely miss smaller lifts. Choose the MDE from business value: the smallest change worth shipping. A smaller MDE needs a much larger sample, roughly growing with one over MDE squared.
39
What is interference or a network effect in experiments, and why does it break standard A/B tests?
Standard A/B analysis assumes one user's outcome does not depend on another user's assignment, the SUTVA assumption. In social or messaging products, a treated user's new feature affects their untreated friends, so control is contaminated and the effect is biased. Solutions include cluster randomisation, where connected groups of users are assigned together, ego-network designs and comparing results across different cluster sizes to estimate the spillover.
40
What is the difference between regression and classification?
Regression predicts continuous values such as price; classification predicts discrete categories such as spam or not spam.
Tips to crack the GlobalLogic Data Science interview
Revise OOP, data structures and one programming language in depth, as GlobalLogic interviews focus on engineering fundamentals.
Learn about its Hitachi Group ownership and work in automotive and embedded software if you have that interest.
Prepare to discuss testing practices, including unit tests and automation, for both developer and QA roles.
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; GlobalLogic interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the GlobalLogic Data Science interview?
Candidates usually go through 4 stages: Online assessment (aptitude + coding); Technical round 1; Technical round 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 GlobalLogic Data Science interview?
This practice set leans on Python for Data Science (9), Machine Learning (8), Deep Learning and NLP (6), SQL for Data Science (4). Revise those first, then practise explaining one or two of your own projects end to end, because most GlobalLogic interviewers ask you to walk through real work.
Are these the exact questions asked at GlobalLogic?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like GlobalLogic's. They are not a leaked or verified list of past questions.
Where does GlobalLogic hire Data Science professionals in India?
GlobalLogic has large teams in Noida, Bengaluru, Gurugram, Hyderabad, Nagpur. Openings and locations change often, so check the GlobalLogic careers page and filter by city and experience.