01
Explain logistic regression.
Show answer
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.
Workday Data Science Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Workday hiring for Data Science roles
Workday is an enterprise cloud software company that provides applications for human resources, finance, planning and payroll. Large organisations use its platform to manage employees, hiring, financial accounting and analytics. It has a growing India presence that hires software engineers, quality engineers, data engineers and support roles, and many organisations also hire Workday functional and technical consultants.
For Data Science roles, this Workday practice set focuses most on Machine Learning (10), SQL for Data Science (6), Computer Vision (5), Python for Data Science (4). It has 3 easy, 25 medium, 12 hard questions, so you can start easy and work up to the harder ones.
- Company type
- Product
- Headquarters
- Pleasanton, California, USA
- Main India offices
- Pune
Workday Data Science interview process
- Online coding assessment
- Technical round 1 (DSA)
- Technical round 2 (design or domain)
- Hiring manager and behavioural round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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02
Write SQL to show each customer's first order date, last order date and the gap between them in days.
Show answer
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
03
Why are NumPy arrays faster than Python lists?
Show answer
They store same-type values in contiguous memory and run vectorised operations in compiled C, avoiding Python loop overhead and per-object type checks.
04
What is a generator and when would you use one?
Show answer
A function using yield that produces values lazily, one at a time. It saves memory when processing large files or streams because the full sequence is never held in RAM.
05
Your LLM app takes 8 seconds to respond and users complain. What do you do?
Show answer
Break down where the time goes: retrieval, model time to first token and generation length. Stream tokens so users see output immediately. Cut prompt and output length, run retrieval and other calls in parallel, and cache frequent answers. Use a smaller or faster model for simple requests, and avoid chains of several LLM calls where one would do. Set a latency budget per step and monitor p95, not just averages.
06
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.
07
Write SQL to find users who logged in on at least three consecutive days.
Show answer
WITH d AS (
SELECT DISTINCT user_id, CAST(login_at AS DATE) AS day
FROM logins
),
g AS (
SELECT user_id, day,
day - CAST(ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY day) AS INT) AS grp
FROM d
)
SELECT DISTINCT user_id
FROM g
GROUP BY user_id, grp
HAVING COUNT(*) >= 3;
-- date minus row number is constant within a streak (PostgreSQL syntax)
08
What is PCA and when do you use it?
Show answer
It projects data onto orthogonal directions of maximum variance to reduce dimensions, remove correlation and speed up models, at some cost of interpretability.
09
What is SHAP and how does it explain model predictions?
Show answer
SHAP assigns each feature a contribution to a single prediction based on Shapley values from game theory. The contributions add up to the difference between the prediction and the average prediction. It gives local explanations, like why one loan was rejected, and global views by aggregating values across rows. TreeSHAP is fast for tree models. Limits include slow computation for other models and misleading results when features are strongly correlated.
10
What is the difference between SGD and Adam optimisers?
Show answer
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.
11
Write pytest unit tests for a feature function that computes days since last purchase.
Show answer
import pandas as pd
from features import days_since_last_purchase
def test_basic_case():
df = pd.DataFrame({'customer_id': [1, 1], 'order_date': pd.to_datetime(['2026-01-01', '2026-01-10'])})
out = days_since_last_purchase(df, as_of='2026-01-15')
assert out.loc[1] == 5
def test_no_future_orders_used():
df = pd.DataFrame({'customer_id': [1], 'order_date': pd.to_datetime(['2026-02-01'])})
out = days_since_last_purchase(df, as_of='2026-01-15')
assert out.empty
12
How would you design an ML platform for a team of 20 data scientists?
Show answer
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.
13
Write SQL to compute funnel conversion from visit to cart to purchase.
Show answer
WITH steps AS (
SELECT user_id,
MAX(CASE WHEN event = 'visit' THEN 1 ELSE 0 END) AS visited,
MAX(CASE WHEN event = 'add_to_cart' THEN 1 ELSE 0 END) AS carted,
MAX(CASE WHEN event = 'purchase' THEN 1 ELSE 0 END) AS purchased
FROM events
WHERE event_date >= '2026-10-01'
GROUP BY user_id
)
SELECT SUM(visited) AS visits, SUM(carted) AS carts, SUM(purchased) AS buyers,
ROUND(100.0 * SUM(carted) / NULLIF(SUM(visited), 0), 1) AS visit_to_cart_pct,
ROUND(100.0 * SUM(purchased) / NULLIF(SUM(carted), 0), 1) AS cart_to_buy_pct
FROM steps;
14
How would you build RFM features for a customer churn model?
Show answer
RFM stands for recency, frequency and monetary value. From the transactions table, for each customer as of a cut-off date: recency is days since the last purchase, frequency is the number of orders in the last 90 days, and monetary is total or average spend in that window. Compute them using only data before the cut-off, add trends like spend this month versus last, and the label from the period after.
15
A feature won its A/B test with a 4 percent lift, but after full launch the metric barely moved. Why might that happen?
Show answer
Several reasons are common. Winner's curse: significant results from underpowered tests tend to overestimate the true effect. Novelty effect faded after launch. Seasonality or other launches changed the baseline, so a before-after comparison is misleading. The test population may differ from all users, for example only one platform. Use a holdout after launch to measure real impact, and run tests with adequate power and duration.
16
You need to use a device_id column with millions of unique values in a streaming model. How would feature hashing help?
Show answer
Feature hashing, the hashing trick, maps categories or tokens into a fixed number of columns using a hash function, without storing a vocabulary. It handles very high-cardinality or ever-changing values, like URLs or device IDs, with constant memory and works well in streaming systems. The trade-off is collisions, where different values share a column, and the features cannot be reversed for explanation. Choose a large enough number of buckets.
17
Why do residual connections make very deep networks easier to train?
Show answer
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.
18
Your manager asks whether collecting more training data will improve the model. How do you answer using a learning curve?
Show answer
A learning curve plots training and validation scores against the size of the training set. If both scores are low and close, the model has high bias and needs more features or a more flexible model. If training is high but validation is much lower and the gap narrows as data grows, the model has high variance and more data will help. It tells you whether collecting more data is worth the cost.
19
What are embeddings and vector databases, and why do LLM apps use them?
Show answer
An embedding model turns text into a vector so that similar meanings are close in space. A vector database stores these vectors with metadata and finds the nearest ones to a query vector quickly using approximate nearest neighbour indexes like HNSW. LLM apps use them for semantic search and retrieval-augmented generation, finding the most relevant documents to put into the prompt. Examples include pgvector, FAISS and dedicated vector stores.
20
What is regularization? Explain L1 vs L2.
Show answer
Regularization adds a penalty on weights to reduce overfitting. L1 (Lasso) adds |w| and can push features to exactly zero. L2 (Ridge) adds w² and shrinks weights smoothly.
21
What is chunking in RAG and how do you choose a chunk size?
Show answer
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.
22
Your dataset is highly imbalanced (2% positives). How do you handle it?
Show answer
Use stratified splits, class weights, resampling (SMOTE or undersampling) and threshold tuning, and judge the model with PR-AUC or F1 instead of accuracy.
23
Your detector finds large objects well but misses small objects like distant people. How do you improve it?
Show answer
Small objects lose detail after downsampling. Train and infer at higher input resolution, or tile large images into overlapping crops. Use a backbone with a feature pyramid so detection happens on high-resolution maps. Tune anchors or assignment settings for small sizes, and oversample images with small objects. Check labelling quality for tiny boxes too. Measure mAP on small objects separately to confirm improvement.
24
Write a scikit-learn ColumnTransformer that preprocesses numeric and categorical columns.
Show answer
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import make_pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
pre = ColumnTransformer([
('num', make_pipeline(SimpleImputer(strategy='median'), StandardScaler()), num_cols),
('cat', make_pipeline(SimpleImputer(strategy='most_frequent'),
OneHotEncoder(handle_unknown='ignore')), cat_cols)
])
model = make_pipeline(pre, LogisticRegression(max_iter=1000))
25
How does the Gini impurity differ from entropy?
Show answer
Both measure node impurity; Gini is cheaper to compute and entropy is slightly more sensitive to class balance, with similar trees in practice.
26
How do you design an A/B test?
Show answer
Define the hypothesis and one primary metric, calculate sample size from the baseline, minimum detectable effect, α and power, randomise users, run for full business cycles, check for sample-ratio mismatch, then test both statistical and practical significance.
27
Write SQL to find the median order value per city.
Show answer
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
28
What is the difference between UNION and UNION ALL?
Show answer
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.
29
How do you pull large data from a SQL database into Pandas efficiently?
Show answer
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.
30
Your notebook runs on your laptop but fails on a colleague's machine. How do you make the work reproducible?
Show answer
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.
31
Tell me about a feature you engineered that significantly improved a model.
Show answer
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.
32
Write scikit-learn code for a pipeline with scaling and logistic regression, evaluated with 5-fold cross-validation.
Show answer
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
pipe = make_pipeline(StandardScaler(), LogisticRegression(max_iter=1000))
scores = cross_val_score(pipe, X, y, cv=5, scoring='roc_auc')
print(scores.mean(), scores.std())
33
What are anchor boxes, and why have anchor-free detectors become popular?
Show answer
Anchor boxes are predefined boxes of several sizes and aspect ratios placed at each grid location; the detector predicts offsets from them and a class score. They need careful tuning to match object shapes in your data and create many redundant predictions. Anchor-free detectors, such as FCOS-style or centre-point methods, predict object centres and box sizes directly. They have fewer hyperparameters, generalise better to unusual shapes and simplify the pipeline.
34
You need forecasts for 50,000 SKUs every week. Would you build one model per series or one global model?
Show answer
One classical model per SKU is simple and interpretable but expensive to maintain, and it fails for short or sparse series. A global model, such as LightGBM trained on all SKUs with lags, calendar and product features, learns shared patterns and handles new items better. In practice I would use a global model as the main engine, keep statistical models for a few high-value series, and compare both in a rolling backtest.
35
How do you profile and speed up slow Python code?
Show answer
Measure first with cProfile or timeit, then replace loops with NumPy or Pandas vectorised calls, use built-ins, caching and better data structures.
36
Your product image classifier works on studio catalogue photos but fails on photos users take with their phones. Why, and what do you do?
Show answer
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.
37
How does a decision tree decide where to split?
Show answer
It tries candidate splits and picks the one that most reduces impurity — Gini or entropy (information gain) for classification, variance / MSE for regression.
38
You must run object detection on an edge camera at 30 frames per second. How do you meet that target?
Show answer
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.
39
Write a Python function to compute MASE for a seasonal forecast.
Show answer
import numpy as np
def mase(y_train, y_true, y_pred, m=7):
# Scale: in-sample error of seasonal naive
y_train = np.asarray(y_train)
scale = np.mean(np.abs(y_train[m:] - y_train[:-m]))
err = np.mean(np.abs(np.asarray(y_true) - np.asarray(y_pred)))
return err / scale
# MASE below 1 beats seasonal naive
40
You must classify support messages into 40 categories, but some categories have only 20 examples. What do you do?
Show answer
Start with a pretrained sentence embedding model plus a simple classifier, or fine-tune a small transformer with class weights. For the rare categories, try few-shot approaches such as SetFit or an LLM with examples in the prompt, and generate or collect more examples. Merge categories that are rarely distinguishable. Report per-class F1 rather than overall accuracy, and route low-confidence predictions to a human queue.
Tips to crack the Workday Data Science interview
- Practise DSA problems and object-oriented design, and be ready to explain your reasoning clearly while coding live.
- Learn basic HR and finance business processes, which helps you understand how Workday applications are used by customers.
- Prepare behavioural stories showing collaboration and customer focus, since Workday highlights its people-first culture and core values.
- 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; Workday interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Workday Data Science interview?
Candidates usually go through 4 stages: Online coding assessment; Technical round 1 (DSA); Technical round 2 (design or domain); Hiring manager and behavioural 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 Workday Data Science interview?
This practice set leans on Machine Learning (10), SQL for Data Science (6), Computer Vision (5), Python for Data Science (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Workday interviewers ask you to walk through real work.
Are these the exact questions asked at Workday?
No. These are curated practice questions that match the skills and difficulty usually tested in product interviews like Workday's. They are not a leaked or verified list of past questions.
Where does Workday hire Data Science professionals in India?
Workday has large teams in Pune. Openings and locations change often, so check the Workday careers page and filter by city and experience.
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