Data Science Interview Questions for Banking & finance Companies
40 questions weighted to the interview style of banking & finance companies — 7 easy, 29 medium, 4 hard.
How banking & finance companies interview for Data Science
Banks and financial-services firms hire for technology, analytics and QA in their global capability centres. Interviews mix technical rounds with domain questions on banking products, regulation, data quality and risk, and they value process discipline.
This set focuses on SQL for Data Science (8), Machine Learning (6), Deep Learning and NLP (5), MLOps and Model Deployment (4).
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01
Your LLM assistant confidently quotes a policy clause that does not exist. Why does this happen and how do you reduce it?
Hallucination is when a model produces confident text that is false or not supported by any source. It happens because the model generates plausible tokens rather than checking facts. Reduce it by grounding answers in retrieved documents, asking the model to cite sources and say I do not know when context is missing, using low temperature, and validating outputs with rules or a second checking step. Measure it with a labelled evaluation set.
02
How is an image represented for a deep learning model?
An image is a numeric array of height by width by channels, where a colour image has three channels, usually RGB, with pixel values from 0 to 255. Libraries differ: OpenCV loads BGR in HWC order, while PyTorch expects tensors in CHW order with a batch dimension in front. Before training you convert to float, scale to 0-1 and normalise with the mean and standard deviation used by the pretrained model.
Use grid search, random search or Bayesian optimisation with cross-validation, starting with a coarse random search and narrowing the promising ranges.
04
What is the difference between UNION and UNION ALL?
Both stack the results of two queries with the same number and type of columns. UNION removes duplicate rows, which needs an extra sort or hash step and is slower. UNION ALL keeps every row, including duplicates, and is faster. Use UNION ALL by default when you know the sets do not overlap or when duplicates matter, such as combining transaction logs from two systems.
05
Write Python code to calculate the sample size per group needed to detect a lift from 5 percent to 5.5 percent conversion.
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.
07
A deep network you built from scratch does not learn at all; the loss stays flat from the first epoch. Could weight initialisation be the reason?
If weights start too large, activations and gradients explode; if too small, they vanish, and training stalls. If all weights are equal, every neuron learns the same thing. Good schemes keep the variance of activations stable across layers: Xavier or Glorot initialisation for tanh and sigmoid, and He initialisation for ReLU. Frameworks use sensible defaults, but it matters when building custom layers or very deep networks.
08
Write Pandas code to create lag and rolling features for each customer.
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())
09
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.
10
What should you log for a production prediction service?
For each request, log a request ID, timestamp, model version, input features or a hash of them, the prediction and score, and latency. Log errors and fallback use separately. Do not log raw personal data unless needed and allowed; mask it. Aggregate these logs into dashboards for traffic, latency percentiles, error rate and score distribution. Joining logged predictions with later outcomes gives the data for monitoring and retraining.
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.
12
Explain exponential smoothing and the Holt-Winters method.
Simple exponential smoothing forecasts with a weighted average of past values where weights decay exponentially, so recent points matter more. Holt's method adds a smoothed trend component. Holt-Winters adds a seasonal component as well, either additive or multiplicative. Each component has a smoothing parameter between 0 and 1 that is fitted to the data. These models are fast, easy to explain and strong baselines for retail and demand data.
13
You target-encoded a city column and validation AUC jumped from 0.72 to 0.95. What do you suspect?
Target encoding replaces each category with the average target value for that category, for example the churn rate per city. It handles high-cardinality columns compactly. The main risk is leakage: if a row's own target is included in its encoding, the model sees the answer and overfits. Prevent it with out-of-fold encoding, smoothing toward the global mean for rare categories, and fitting only on training data.
14
Write NumPy code for non-maximum suppression on a set of boxes and scores.
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;
16
Your image classifier trained on 2,000 images memorises the training set within 5 epochs. What do you try?
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.
17
What is the difference between hard voting and soft voting in ensembles?
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.
18
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
19
Write SQL to find the median order value per city.
SELECT city,
PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY amount) AS median_amount
FROM orders
GROUP BY city;
-- PERCENTILE_CONT interpolates; PERCENTILE_DISC returns an actual value
20
Write SQL to find duplicate emails in a users table.
Situation: the team was confident a redesigned home page would increase orders. Task: I owned the experiment design and analysis. Action: I set conversion as primary and session length and refunds as guardrails, ran it for two full weeks and found no lift in orders but a drop for older app versions. Result: we fixed the performance issue, retested, and shipped a version that raised orders by about 1.5 percent.
22
Write a prompt template for classifying customer support tickets.
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.
23
What is the difference between ROW_NUMBER, RANK and DENSE_RANK?
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.
24
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.
25
Your fraud model has 99 percent accuracy, but the business says it catches almost no fraud. Why?
If only 1 percent of transactions are fraud, a model that predicts not fraud for everything is 99 percent accurate and useless. Accuracy hides the minority class. I would check the confusion matrix, recall on fraud cases and precision-recall AUC. Then fix it with class weights, resampling or a lower decision threshold, and choose the threshold based on the cost of missed fraud versus false alerts.
26
Tell me about a time you deployed a model under a tight deadline.
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.
27
Your random forest is accurate but too slow at prediction time for a real-time API. What do you do?
First measure where the time goes. Then reduce trees and maximum depth and check the accuracy loss. Switch to a gradient boosting model, which often needs fewer, smaller trees. Compile the model with tools like ONNX Runtime or Treelite. Cache features or precompute scores for known users. If accuracy allows, a simpler logistic regression may meet the latency budget easily. Always compare options on the same validation set.
28
Soon after a new model version went live, customer complaints spiked. How do you roll back safely and learn from it?
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.
29
Your feature computation on millions of rows uses only one CPU core. Would you use threads or processes to speed it up?
Because of the GIL, threads in CPython do not run Python bytecode in parallel. Threads are fine for I/O-bound work like calling APIs or reading many files. For CPU-bound work like feature computation on chunks, multiprocessing or joblib runs separate processes on separate cores. The cost is process start-up time and copying data between processes. Many NumPy and Pandas operations already release the GIL internally, so measure before adding complexity.
30
What do temperature and top-p control in LLM generation?
Both control randomness when the model picks the next token. Temperature scales the probabilities: low values like 0 to 0.3 make output focused and repeatable, high values make it more varied and creative. Top-p, nucleus sampling, keeps only the smallest set of tokens whose probabilities add up to p, such as 0.9, and samples from them. For extraction and classification use low temperature; for brainstorming use higher values.
They store same-type values in contiguous memory and run vectorised operations in compiled C, avoiding Python loop overhead and per-object type checks.
32
You assign values to a filtered DataFrame and Pandas shows a SettingWithCopyWarning. What is going on and how do you fix it?
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.
33
How do you use groupby with multiple aggregations?
Package the model behind an API with versioning, track latency and errors, monitor input drift and prediction quality, and automate retraining with rollback.
ReLU for hidden layers, sigmoid for binary outputs, softmax for multi-class outputs and tanh when zero-centred outputs help; Leaky ReLU avoids dead neurons.
39
What is DBSCAN and when is it better than K-means?
DBSCAN is a density-based clustering algorithm. It groups points that have at least min_samples neighbours within distance eps, and marks isolated points as noise. Unlike K-means, it does not need the number of clusters, finds clusters of any shape, and handles outliers. It struggles when clusters have very different densities and in high dimensions. It suits location data, like finding delivery hotspots from GPS points.
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.
How are Data Science interviews at banking & finance companies different?
Banks and financial-services firms hire for technology, analytics and QA in their global capability centres. Interviews mix technical rounds with domain questions on banking products, regulation, data quality and risk, and they value process discipline.
Which banking & finance companies are covered?
This page links to 18 banking & finance companies, including Goldman Sachs, JP Morgan Chase, Morgan Stanley, Barclays, HSBC, American Express, Wells Fargo, Citi. Each has its own page with rounds and a separate practice set.
What rounds should I expect?
Common rounds at these companies include: HR round; Managerial round; Technical round; Hiring manager round; Online assessment (aptitude + technical). The exact process varies by company and role.