Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About PwC hiring for Data Science roles
PwC is one of the Big Four professional services networks, offering assurance, tax, consulting and deals services. In India, PwC member firms and acceleration centres support global clients with technology consulting, data analytics, cyber, risk and process work. It hires for associate and consultant roles in analytics, BI, testing, ERP and risk assurance.
For Data Science roles, this PwC practice set focuses most on Time Series & Forecasting (6), Generative AI and LLMs (5), SQL for Data Science (5), Python for Data Science (4). It has 7 easy, 30 medium, 3 hard questions, so you can start easy and work up to the harder ones.
Company type
Consulting
Headquarters
London, United Kingdom
Main India offices
Kolkata, Bengaluru, Mumbai, Hyderabad, Gurugram
PwC Data Science interview process
Online assessment (aptitude + technical)
Group discussion or case round
Technical interview
Partner or HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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01
Write Python code to compute the haversine distance between customer and store locations.
An iterable can produce an iterator (it has __iter__); an iterator has __next__ and remembers its position. A for-loop calls iter() on the iterable and then next() repeatedly.
03
What is stationarity and why do many forecasting models need it?
A stationary series has a constant mean, constant variance and an autocorrelation structure that does not change over time. Classical models like ARMA assume stationarity because they learn fixed relationships between past and present values; if the mean keeps drifting, those relationships break. You usually make a series stationary by differencing to remove trend, seasonal differencing to remove seasonality, and a log transform to stabilise growing variance.
04
Your forecast uses actual daily temperature as a feature and looks great in backtesting, but it performs worse in production. Why?
In backtesting you used the actual temperature for future days, which you will never know at forecast time. In production the model gets a weather forecast, which has its own error, so performance drops. This is a subtle form of leakage. Fix the backtest by using the weather forecast that was available on each forecast date, or lagged actuals, so offline results match what production can really achieve.
05
What is gradient descent and what does the learning rate do?
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.
An AI agent is an LLM that works in a loop: it plans a step, calls a tool, reads the result and decides the next step until the goal is met. For example, it might query a database, run Python on the result and write a summary. Agents handle multi-step tasks but are less predictable, slower and costlier than a single call. Good designs limit tools and steps, log every action and keep a human check for risky actions.
07
What is a time series and how is it different from ordinary tabular data?
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.
Pearson measures linear relationships on continuous data; Spearman uses ranks, so it captures monotonic relationships and is less sensitive to outliers.
09
Your RAG chatbot often answers using the wrong documents. How do you fix it?
First check retrieval on its own with a labelled set of questions, since generation cannot fix bad context. Then improve chunking so passages are self-contained, add metadata filters such as product or date, try a better embedding model, and add hybrid search with keywords. A reranker on the top 20 results often helps most. Also rewrite vague user questions into clearer search queries before retrieval.
10
How do you evaluate and explain a model to business stakeholders?
Tie metrics to business outcomes such as cost saved, show feature importance or SHAP values, give example predictions and be transparent about limitations.
L1 (Lasso) adds absolute weights and can shrink some to zero, giving feature selection. L2 (Ridge) adds squared weights and shrinks them smoothly without removing features.
12
Tell me about a time you explained the limits of GenAI to business stakeholders.
Situation: sales leaders wanted an LLM to auto-send pricing quotes to clients. Task: guide them to a safe plan. Action: I demoed real failure cases where the model invented discounts, showed accuracy numbers from a test set and proposed a draft-and-approve flow with fixed price data from our system. Result: they agreed, quote drafting time fell by half, and no wrong prices reached clients.
13
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.
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.
15
Most of your products sell zero units on most days, with occasional sales. Which forecasting approach suits this?
This is intermittent demand, and standard models like ARIMA forecast a small constant that is useless for stocking. Croston's method and its SBA variant forecast the size of non-zero demand and the interval between sales separately. Alternatives are aggregating to weekly level, or using a global gradient boosting model with Tweedie or Poisson loss. Evaluate with MAE or MASE, not MAPE, because zero actuals break percentage errors.
16
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.
Calibration checks whether predicted probabilities match real frequencies (e.g. 70% predictions are right about 70% of the time). Fix with Platt scaling or isotonic regression.
18
How would you quickly build a sentiment analysis model for product reviews?
Step 1: collect and label a sample of reviews, checking class balance. Step 2: build a baseline with TF-IDF and logistic regression to get a quick benchmark. Step 3: fine-tune a small pretrained transformer such as DistilBERT for better accuracy, or use a multilingual model if reviews mix languages. Step 4: evaluate with F1 per class and read wrong predictions. Step 5: handle sarcasm and neutral reviews explicitly.
19
A feature won its A/B test with a 4 percent lift, but after full launch the metric barely moved. Why might that happen?
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.
20
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.
21
After joining orders with a promotions table, total revenue doubled. What went wrong?
The join created duplicate order rows, because some orders match more than one promotion row, so each amount is counted several times. Check by comparing COUNT(*) with COUNT(DISTINCT order_id) after the join. Fix it by aggregating the promotions table to one row per order first, picking one promotion with ROW_NUMBER, or summing revenue before the join. Always check row counts after every join.
They store same-type values in contiguous memory and run vectorised operations in compiled C, avoiding Python loop overhead and per-object type checks.
23
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.
24
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.
25
What is chunking in RAG and how do you choose a chunk size?
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.
26
Write NumPy code to compute the Dice coefficient between a predicted and a true binary mask.
Polynomial features add powers and products of existing features, like x squared and x times y, so a linear model can fit curves and interactions. The risk is that the number of features grows very fast with degree and input count, causing overfitting, slow training and multicollinearity. Use a low degree, scale features first, and add regularisation such as Ridge or Lasso to keep coefficients under control.
28
Describe a time you had to push back on or prioritise many ad-hoc data requests.
Situation: as the only analyst for three teams, I received 15 ad-hoc SQL requests a week and my model work stalled. Task: protect focus without upsetting stakeholders. Action: I grouped requests, built a self-serve dashboard covering the 5 most common questions, and set up a simple weekly priority review with the managers. Result: ad-hoc requests dropped by 60 percent and the model shipped a month later as planned.
29
What are quantile and power transforms such as Box-Cox and Yeo-Johnson, and when do you use them?
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.
30
How do you make a machine learning experiment reproducible?
Version the code with Git, the data with a tool like DVC or dated snapshots, and the environment with pinned requirements or Docker. Set random seeds for every library. Log parameters, metrics and the exact data version for each run with a tracker like MLflow. Keep preprocessing inside a pipeline rather than manual notebook steps. Then anyone should be able to rebuild the same model from a commit and a data version.
31
Tell me about a time you created documentation that helped your team work with data.
Situation: new analysts kept misusing two similar revenue columns. Task: reduce repeated questions and errors. Action: I wrote a short data dictionary for our main tables, with definitions, known pitfalls and sample queries, and linked it in the team wiki and onboarding checklist. I updated it whenever schemas changed. Result: onboarding time dropped, and the number of revenue mismatches in reports fell noticeably within a quarter.
Term Frequency × Inverse Document Frequency: a word scores high if it is frequent in one document but rare across all documents. A classic way to turn text into numeric features.
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.
34
Your classifier scores 98 percent on validation, but Grad-CAM shows it focuses on a watermark rather than the object. What is happening?
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.
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.
38
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.
39
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.
40
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.
Tips to crack the PwC Data Science interview
Learn about the PwC Professional framework, which covers whole leadership, business acumen, technical capabilities and relationships.
Practise case questions and guesstimates with a clear structure, especially for consulting and advisory roles.
Revise Excel, SQL and a BI tool, and be ready to describe how analysis helped a decision.
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; PwC interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the PwC Data Science interview?
Candidates usually go through 4 stages: Online assessment (aptitude + technical); Group discussion or case round; Technical interview; Partner or 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 PwC Data Science interview?
This practice set leans on Time Series & Forecasting (6), Generative AI and LLMs (5), SQL for Data Science (5), Python for Data Science (4). Revise those first, then practise explaining one or two of your own projects end to end, because most PwC interviewers ask you to walk through real work.
Are these the exact questions asked at PwC?
No. These are curated practice questions that match the skills and difficulty usually tested in consulting interviews like PwC's. They are not a leaked or verified list of past questions.
Where does PwC hire Data Science professionals in India?
PwC has large teams in Kolkata, Bengaluru, Mumbai, Hyderabad, Gurugram. Openings and locations change often, so check the PwC careers page and filter by city and experience.