01
What is a Poisson distribution used for?
Show answer
Modelling the count of events in a fixed interval when events occur independently at a constant average rate, such as calls per hour or defects per batch.
UST Data Science Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About UST hiring for Data Science roles
UST is a digital transformation and IT services company with large delivery centres in India, particularly in Kerala. It provides software development, testing, cloud, data and AI services for clients in healthcare, retail, banking and semiconductors. It hires in India for software engineers, test engineers, data engineers and analysts.
For Data Science roles, this UST practice set focuses most on Statistics and Probability (8), Machine Learning (6), Feature Engineering (5), SQL for Data Science (4). It has 23 easy, 17 medium questions, so you can start easy and work up to the harder ones.
- Company type
- IT services
- Headquarters
- Aliso Viejo, California, USA
- Main India offices
- Thiruvananthapuram, Kochi, Bengaluru, Chennai, Hyderabad
UST Data Science interview process
- Online assessment (aptitude + reasoning)
- Coding test
- Technical interview
- HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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02
A query using NOT IN returns zero rows even though matches should exist. What is the likely cause, and how do NULLs behave in SQL?
Show answer
NULL means unknown, so any comparison with NULL, even NULL = NULL, gives unknown and the row is filtered out. Use IS NULL or IS NOT NULL instead. Aggregates like SUM, AVG and COUNT(column) skip NULLs, so AVG may be computed over fewer rows than you expect. NOT IN with a subquery that contains a NULL returns no rows. COALESCE replaces NULLs with a default value.
03
What is the difference between regression and classification?
Show answer
Regression predicts continuous values such as price; classification predicts discrete categories such as spam or not spam.
04
How do you detect outliers?
Show answer
Use the IQR rule (below Q1-1.5 IQR or above Q3+1.5 IQR), z-scores beyond 3, box plots or model-based methods such as Isolation Forest, then decide whether to cap, remove or keep them.
05
Tell me about a time you found a data quality issue using SQL.
Show answer
Situation: during my internship I was building a monthly sales report. Task: make sure numbers were correct before sharing. Action: I compared order counts per day and noticed one day had double the usual orders. A GROUP BY on order_id showed every order that day appeared twice because a load job had run twice. Result: I reported it, the data team removed duplicates and added a uniqueness check. Now I always validate row counts.
06
Pearson vs Spearman correlation?
Show answer
Pearson measures linear relationships on continuous data; Spearman uses ranks, so it captures monotonic relationships and is less sensitive to outliers.
07
What is differencing in time series analysis?
Show answer
Differencing replaces each value with the change from the previous value: y_t minus y_(t-1). It removes a linear trend and often makes a series stationary. Seasonal differencing subtracts the value one season back, such as y_t minus y_(t-7) for daily data with a weekly cycle. Avoid over-differencing, because it adds noise and negative autocorrelation. In ARIMA, the d parameter is the number of times you difference.
08
Annotators labelling your images often disagree with each other. How do you handle this?
Show answer
First measure agreement, for example with Cohen's kappa or IoU between annotators' boxes, to find which classes are confusing. Usually the guidelines are unclear, so write precise rules with example images for edge cases and retrain annotators. Have multiple people label a subset and resolve conflicts by majority vote or an expert reviewer. Track ambiguous images separately, since even a perfect model cannot beat human label noise.
09
Write a function to calculate RMSE without any library.
Show answer
def rmse(y, p):
return (sum((a - b) ** 2 for a, b in zip(y, p)) / len(y)) ** 0.5
10
What is standard error and how is it different from standard deviation?
Show answer
Standard deviation measures how spread out individual data points are. Standard error measures how much a sample statistic, usually the mean, would vary if you repeated the sampling. For the mean, SE = SD divided by the square root of n. So with more data the standard deviation stays roughly the same, but the standard error shrinks. Confidence intervals and t-tests are built on the standard error.
11
What is gradient descent and what does the learning rate do?
Show answer
An optimisation method that updates weights in the opposite direction of the loss gradient. The learning rate sets the step size: too high and it diverges, too low and it is slow. Variants include SGD, mini-batch and Adam.
12
Tell me about a time you chose a simpler model over a more complex one.
Show answer
Situation: for a credit risk use case, my deep learning model beat logistic regression by only 0.5 AUC points. Task: recommend one for production. Action: I compared explainability, latency, monitoring effort and regulatory needs, and showed the small gain was within noise across folds. Result: we shipped a scorecard-style logistic regression that the risk team could audit. It launched three weeks earlier and was easy to maintain.
13
What is the difference between zero-shot and few-shot prompting?
Show answer
In zero-shot prompting you give only instructions, like classify this review as positive or negative. In few-shot prompting you also include a few worked examples of input and correct output, which shows the model the format and decision boundary. Few-shot usually improves consistency on custom labels, at the cost of more tokens. Pick examples that cover tricky cases and keep their format identical to what you want back.
14
What is tokenisation in NLP?
Show answer
Splitting text into units such as words, subwords or characters that are mapped to ids; subword methods like BPE handle rare words well.
15
You must forecast demand for a new product that has only three weeks of history. What do you do?
Show answer
Three weeks is too little to learn seasonality, so borrow information. Find similar past launches by category, price band and channel, and use their early sales curves as analogues. A global model trained across many products, with product attributes as features, can also forecast a cold-start item. Combine this with planned marketing spend and business judgement, show a wide interval, and update the forecast weekly as real data arrives.
16
Explain precision vs recall. When would you prefer recall?
Show answer
Precision = TP / (TP + FP). Recall = TP / (TP + FN).
Prefer recall when missing a positive is costly — for example disease screening or fraud detection.
17
What are quantile and power transforms such as Box-Cox and Yeo-Johnson, and when do you use them?
Show answer
Power transforms reshape a skewed feature to look more normal. Box-Cox needs strictly positive values, while Yeo-Johnson also handles zero and negative values; both learn a lambda parameter from training data. A quantile transform maps values to a uniform or normal distribution by rank, which is robust to outliers. They help linear models, distance-based methods and neural networks, but tree models rarely need them.
18
What is feature scaling and which models need it?
Show answer
Putting features on comparable ranges using standardisation or min-max scaling. Distance-based and gradient-based models (KNN, SVM, regression, neural nets) need it; tree models do not.
19
Write SQL to calculate month-over-month revenue growth.
Show answer
WITH monthly AS (
SELECT DATE_TRUNC('month', order_date) AS month, SUM(amount) AS revenue
FROM orders
GROUP BY 1
)
SELECT month, revenue,
LAG(revenue) OVER (ORDER BY month) AS prev_revenue,
ROUND(100.0 * (revenue - LAG(revenue) OVER (ORDER BY month))
/ NULLIF(LAG(revenue) OVER (ORDER BY month), 0), 2) AS mom_pct
FROM monthly;
20
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.
21
What is NumPy broadcasting?
Show answer
The rule that lets arrays of different shapes operate together by virtually stretching the smaller one, for example adding a scalar or a (3,) vector to a (4,3) matrix.
22
Write Pandas code to create useful features from a datetime column.
Show answer
df['ts'] = pd.to_datetime(df['ts'])
df['hour'] = df['ts'].dt.hour
df['dayofweek'] = df['ts'].dt.dayofweek
df['is_weekend'] = df['dayofweek'].isin([5, 6]).astype(int)
df['month'] = df['ts'].dt.month
df['days_since_signup'] = (df['ts'] - df['signup_date']).dt.days
23
Write a Python function to flatten a nested list of any depth.
Show answer
def flatten(items):
out = []
for x in items:
if isinstance(x, list):
out.extend(flatten(x))
else:
out.append(x)
return out
print(flatten([1, [2, [3, 4]], 5])) # [1, 2, 3, 4, 5]
24
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])}
25
What are decorators in Python?
Show answer
A decorator wraps a function to add behaviour such as logging, timing or caching without changing its code. It is applied with the @name syntax above the function.
26
What is data augmentation?
Show answer
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.
27
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'
);
28
What is the difference between one-hot, label and ordinal encoding?
Show answer
One-hot encoding creates a binary column per category and suits nominal data like city for linear models. Label encoding maps categories to integers arbitrarily; it is fine for tree models but implies a false order for linear models. Ordinal encoding also uses integers but in a meaningful order, like low, medium, high mapped to 0, 1, 2. Choose based on whether categories have a natural order and which model you use.
29
What is conditional probability? Give an example.
Show answer
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.
30
Your model performance degrades every few months. Would you retrain on a fixed schedule or based on triggers?
Show answer
Scheduled retraining, like monthly, is simple and predictable but may be too late after a sudden change or wasteful when nothing changed. Trigger-based retraining runs when monitoring shows drift or a performance drop, which reacts faster but needs reliable monitoring. Many teams combine both: a regular schedule plus triggers for big shifts. Either way, a retrained model must pass the same evaluation gate before replacing the current one.
31
What is skewness?
Show answer
A measure of asymmetry. Right-skewed data has a long tail on the right (mean greater than median); left-skewed is the opposite. Log transforms often reduce right skew.
32
Correlation vs covariance?
Show answer
Covariance shows the direction of a relationship but depends on units. Correlation is covariance scaled to −1…+1, so it shows direction and strength and is unit-free.
33
What is mean Average Precision (mAP) in object detection?
Show answer
For each class, detections are ranked by confidence and a precision-recall curve is computed, where a detection is a true positive if its IoU with an unmatched ground truth box passes a threshold. Average Precision is the area under that curve. mAP is the mean across classes. mAP@0.5 uses IoU 0.5, while mAP@0.5:0.95 averages over thresholds from 0.5 to 0.95 and rewards tighter boxes.
34
What is the difference between supervised and unsupervised learning?
Show answer
Supervised learning trains on labelled data to predict a target; unsupervised learning finds structure such as clusters or components in unlabelled data.
35
What is an Airflow DAG?
Show answer
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.
36
Write Pandas code to cap outliers at the 1st and 99th percentile.
Show answer
# fit limits on training data only, then apply to test
low, high = train['income'].quantile([0.01, 0.99])
train['income_capped'] = train['income'].clip(low, high)
test['income_capped'] = test['income'].clip(low, high)
37
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.
38
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.
39
What are trend, seasonality and noise in a time series?
Show answer
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.
40
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.
Tips to crack the UST Data Science interview
- Practise aptitude and coding questions for the assessment, focusing on accuracy within time limits.
- Revise SQL, testing concepts and your main programming language, and prepare examples from your projects.
- Learn about UST's history as UST Global and its large Thiruvananthapuram campus for company questions.
- 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; UST interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the UST Data Science interview?
Candidates usually go through 4 stages: Online assessment (aptitude + reasoning); Coding test; Technical interview; 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 UST Data Science interview?
This practice set leans on Statistics and Probability (8), Machine Learning (6), Feature Engineering (5), SQL for Data Science (4). Revise those first, then practise explaining one or two of your own projects end to end, because most UST interviewers ask you to walk through real work.
Are these the exact questions asked at UST?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like UST's. They are not a leaked or verified list of past questions.
Where does UST hire Data Science professionals in India?
UST has large teams in Thiruvananthapuram, Kochi, Bengaluru, Chennai, Hyderabad. Openings and locations change often, so check the UST careers page and filter by city and experience.
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