Quantiphi Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Quantiphi hiring for Data Analytics roles
Quantiphi is an AI-first digital engineering company that builds machine learning, data engineering, cloud and applied AI solutions for enterprises. It is known for close partnerships with major cloud providers and works with clients in healthcare, banking, media and retail. In India it hires machine learning engineers, data engineers, cloud engineers and software developers for global projects.
For Data Analytics roles, this Quantiphi practice set focuses most on Statistics and Case Studies (8), Power BI (6), Python for Analysis (5), A/B Testing & Experiments (5). It has 4 easy, 34 medium, 2 hard questions, so you can start easy and work up to the harder ones.
Company type
Analytics
Headquarters
Marlborough, Massachusetts, USA
Main India offices
Mumbai, Bengaluru
Quantiphi Data Analytics interview process
Online assessment (aptitude + coding + ML basics)
Technical interview 1
Technical interview 2
HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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01
Checkout conversion fell from 3.2% to 2.6% the week after an app release. How do you investigate?
First confirm it is real: check tracking events, data delays and whether the denominator changed. Then segment by app version, platform, device, payment method and traffic source. If the drop is concentrated in the new version or one payment option, it points to a bug or UX change. Look at funnel steps to find where users drop. Compare with seasonality and campaigns, then share findings with product and engineering quickly.
02
What does value_counts do in pandas, and how do you show percentages instead of counts?
value_counts returns how many times each unique value appears in a Series, sorted from most to least frequent. It is a quick way to check category distribution or spot typos. Pass normalize=True to get proportions, then multiply by 100 for percentages, for example df['city'].value_counts(normalize=True).mul(100).round(1). Use dropna=False to include missing values, which are excluded by default and are often important.
03
What steps would you apply in Power Query to clean a messy system export?
Typical steps: remove top junk rows and promote the real header row, remove blank rows and columns, set correct data types, trim and clean text, split combined columns such as Name - City, replace error values, fill down for grouped labels, remove duplicates on the business key, and unpivot wide month columns. Rename steps meaningfully so others can follow them, and keep the query foldable when the source is a database.
04
Write an Excel formula to put invoices into ageing buckets (0-30, 31-60, 61-90, 90+ days).
-- C2 = invoice date, TODAY() = as-of date
=LET(age, TODAY()-C2,
IFS(age<=30, "0-30",
age<=60, "31-60",
age<=90, "61-90",
TRUE, "90+"))
-- then a PivotTable with bucket in rows and SUM of amount gives the ageing report
05
Write a DAX measure for cumulative sales from the start of the data to the selected date.
Cumulative Sales =
VAR maxDate = MAX('Date'[Date])
RETURN
CALCULATE(
[Total Sales],
FILTER(ALL('Date'[Date]), 'Date'[Date] <= maxDate)
)
-- for a running total that resets each year use TOTALYTD([Total Sales], 'Date'[Date])
06
Write pandas code to convert daily sales into weekly totals, showing zero for days with no sales.
State assumptions clearly. Of 1 crore people, assume 70 percent drink tea, so 70 lakh. Say half of their cups are bought outside, at an average of one bought cup per drinker every day for that half, giving about 35 lakh purchased cups, with more at offices and stations. So roughly 30 to 40 lakh cups. Interviewers judge the structure, segmentation and sanity check more than the exact number.
08
A stakeholder says the Power BI report shows different revenue than their Excel file. How do you troubleshoot?
I would first agree on definitions: gross or net, including tax or returns, order date or invoice date. Then compare one small slice, such as one month and one region, in both. I check filters on the report and page, relationships that drop unmatched rows, refresh time versus the Excel extract time, and duplicate rows. Once the gap is explained, I document the definition in the report so the question does not repeat.
09
Why are A/B tests tricky in two-sided marketplaces such as ride-hailing or food delivery?
In marketplaces, groups share supply, so the treatment can affect control. If treatment riders get a discount and book more, fewer drivers remain for control riders, which makes treatment look better than it would at full rollout. This violates the independence assumption. Teams use cluster randomisation by city or region, switchback tests that alternate treatment by time slots, or model the spillover when estimating impact.
10
Your Tableau numbers change unexpectedly when a filter is applied to a view that uses a FIXED LOD. Why?
Usually it is the order of operations. FIXED LODs are computed before dimension filters, so they ignore a normal region filter, while other measures in the view respect it. That mix makes percentages and totals look inconsistent. Decide what the business wants: if the LOD should respect the filter, add the filter to context; if it should not, keep it and label it clearly, for example share of national sales.
A confidence interval gives a range of plausible values for a population parameter based on a sample. A 95 percent interval for average order value of 820 to 880 rupees means that if we repeated the sampling many times, about 95 percent of such intervals would contain the true average. Wider intervals mean more uncertainty; larger samples narrow them. Reporting the interval is more honest than reporting one number.
Situation: three managers asked for new dashboards in the same week. Task: I could not finish all of them on time. Action: I asked each about the decision the dashboard would support and its deadline, estimated effort, and shared a proposed order with my manager. I gave one requester a quick interim extract. Result: the most urgent dashboard, for a pricing review, shipped first, and nobody was left without any data.
14
What is the difference between Goal Seek and Solver?
Goal Seek changes one input to hit a target output; Solver optimises with several variables and constraints, for example maximising profit under budget limits.
15
Write Python code to test whether conversion differs between control and treatment.
from statsmodels.stats.proportion import proportions_ztest, proportion_confint
conversions = [1180, 1295] # control, treatment
visitors = [30000, 30100]
z, p = proportions_ztest(conversions, visitors)
print(f'z = {z:.2f}, p = {p:.4f}')
for c, n in zip(conversions, visitors):
low, high = proportion_confint(c, n, method='wilson')
print(f'rate {c/n:.3%} 95% CI {low:.3%} to {high:.3%}')
16
What is the difference between users, sessions and events in Google Analytics 4?
GA4 uses an event-based model: every interaction, such as page_view, scroll, add_to_cart or purchase, is an event with parameters. A session is a group of events from one user within a period of activity, ending after 30 minutes of inactivity by default. A user is identified by a device cookie, app instance or a logged-in user ID. One user can have many sessions, and each session many events.
Tidy data follows three rules: each variable is a column, each observation is a row, and each type of observational unit is a table. A sheet with months as columns is not tidy; unpivoting it to Region, Month and Sales columns makes it tidy. Tidy data is easy to filter, group, join and chart in pandas, SQL, Power BI and Tableau, so reshaping into this form is a core cleaning step.
18
Tell me about a time an experiment result went against what the team expected.
Situation: the team was sure a shorter signup form would raise registrations. Task: I owned the test analysis. Action: registrations rose 6%, but I checked the guardrail and found seven-day activation fell, because we had removed a question that personalised onboarding. I shared both results with the segment breakdown. Result: we kept the short form but moved the question to the first screen after signup, which recovered activation.
19
Write an Excel formula to look up a price using two criteria, product and city.
=XLOOKUP(1, (A2:A500=G2) * (B2:B500=H2), C2:C500, "Not found")
-- A = product, B = city, C = price
-- G2 and H2 hold the product and city to find
-- each condition returns TRUE/FALSE; multiplying gives 1 only where both match
20
What are statistical power and minimum detectable effect (MDE)?
Power is the probability that the test detects a real effect of a given size, commonly set at 80%. The minimum detectable effect is the smallest lift you care about and can reliably detect with your sample size. Smaller MDEs, lower baseline rates and higher power all need more users. If you can only get 10,000 users a week, you may only be able to detect large lifts, which should shape what you test.
21
Write a Tableau calculation to find each customer's first purchase date and flag new versus repeat orders.
// First Purchase Date
{ FIXED [Customer ID] : MIN([Order Date]) }
// Order Type
IF [Order Date] = [First Purchase Date]
THEN 'New'
ELSE 'Repeat'
END
// Cohort Month for retention analysis
DATETRUNC('month', [First Purchase Date])
An average can improve while the tail gets worse. I would look at the distribution: median, 90th and 95th percentiles and the share of orders beyond the promised time. Then break it down by city, time slot and restaurant to see where late orders cluster. Also check whether promised times changed, whether complaint tracking changed and whether a few very fast orders pulled the mean down.
25
Write pandas code to fill missing values in a column with the median of each group.
# Fill missing delivery_days with the median for that city
df['delivery_days'] = (
df['delivery_days']
.fillna(df.groupby('city')['delivery_days'].transform('median'))
)
# Rows in a city with no values at all stay NaN; fall back to overall median
df['delivery_days'] = df['delivery_days'].fillna(df['delivery_days'].median())
It extracts and transforms data using recorded steps in M language, such as merging, unpivoting, changing types and removing errors, before loading to the model.
27
Write pandas code to compute month-on-month percentage change and cumulative sales for each store.
df = df.sort_values(['store', 'month'])
df['mom_pct'] = (
df.groupby('store')['sales'].pct_change() * 100
).round(1)
df['cum_sales'] = df.groupby('store')['sales'].cumsum()
# first month of each store has NaN for mom_pct, which is expected
28
Write a Tableau calculated field that keeps only sales from the last 30 days.
// Calculated field: Last 30 Days Flag
DATEDIFF('day', [Order Date], TODAY()) <= 29
AND [Order Date] <= TODAY()
// Put this field on Filters and keep True.
// For a sales measure instead of a filter:
// IF DATEDIFF('day', [Order Date], TODAY()) <= 29 THEN [Sales] END
29
How would you investigate a sudden 20% drop in daily sales?
Check data pipelines first, then segment by channel, region, product and device, compare to the same period last year, and look for launches, outages or campaigns.
30
When would you use a one-tailed test instead of a two-tailed test?
A two-tailed test checks for a difference in either direction and is the safer default for A/B tests. A one-tailed test checks only one direction, such as treatment being better, and needs fewer users. Use it only if you decided beforehand that you would act the same way whether the change has no effect or a negative effect. Otherwise you may miss harm, which is why most teams stay two-tailed.
A calculated column is computed row by row and stored in the model. A measure is computed at query time using the filter context, e.g. Total Sales = SUM(Sales[Amount]). Prefer measures for aggregations.
32
You must present to the sales team that they missed their quarterly target by 18%. How do you handle it?
Lead with the fact clearly, then show where the gap came from, such as which regions, products or deal stages, so it becomes actionable rather than a blame session. Separate controllable causes like lower conversion from uncontrollable ones like a delayed product launch. Highlight what did work, and end with two or three specific actions and the metrics to track them. Share the numbers with sales leaders privately first, to avoid surprises.
33
Tell me about a time you automated a manual Excel task.
Situation: our team spent about four hours every Monday copying sales exports from five branches into one summary sheet. Task: I was asked to cut that time. Action: I built a Power Query that read every file in a shared folder, cleaned headers and appended them, then a PivotTable and a few formulas produced the summary on refresh. Result: the report took ten minutes, copy-paste errors stopped, and two other teams reused the template.
34
What is method chaining in pandas and why is it useful?
Method chaining writes a sequence of transformations as one expression, each step returning a new DataFrame, for example df.query('sales > 0').assign(margin=lambda x: x.profit / x.sales).groupby('region').agg(total=('sales', 'sum')). It avoids many temporary variables, reads top to bottom like a recipe, and reduces SettingWithCopy issues. Wrap the chain in brackets with one step per line, and use pipe for custom functions.
35
What is DAX and how does it differ from Excel formulas?
Achievement % =
DIVIDE([Total Sales], [Sales Target])
KPI Colour =
VAR a = [Achievement %]
RETURN
SWITCH(
TRUE(),
ISBLANK(a), "#9E9E9E",
a >= 1, "#2E7D32",
a >= 0.9, "#F9A825",
"#C62828"
)
-- apply KPI Colour via Format > Callout value > fx > Field value
The Central Limit Theorem says that the distribution of sample means approaches a normal distribution as sample size grows, usually from about 30 observations, even if the underlying data is skewed. This is why we can use z-tests, t-tests and confidence intervals for averages such as order value or session time. It does not make the raw data normal, and very heavy-tailed data may need larger samples.
I would ask which decisions each new visual supports and how often they will look at it. Usually a few are essential and the rest are nice to have. I would propose keeping the main page focused on core KPIs and moving detail into drill-through pages, tooltips or a separate analysis tab. Showing a quick mock-up of both versions helps them see that the cleaner page answers questions faster.
Tips to crack the Quantiphi Data Analytics interview
Revise machine learning fundamentals such as regression, classification, evaluation metrics and overfitting, with clear intuitive explanations.
Get hands-on with at least one cloud platform and mention any cloud certifications, since much client work runs on cloud services.
Prepare a deep explanation of one ML or data project, including dataset, model choice, results and deployment approach.
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; Quantiphi interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Quantiphi Data Analytics interview?
Candidates usually go through 4 stages: Online assessment (aptitude + coding + ML basics); Technical interview 1; Technical interview 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 Quantiphi Data Analytics interview?
This practice set leans on Statistics and Case Studies (8), Power BI (6), Python for Analysis (5), A/B Testing & Experiments (5). Revise those first, then practise explaining one or two of your own projects end to end, because most Quantiphi interviewers ask you to walk through real work.
Are these the exact questions asked at Quantiphi?
No. These are curated practice questions that match the skills and difficulty usually tested in analytics interviews like Quantiphi's. They are not a leaked or verified list of past questions.
Where does Quantiphi hire Data Analytics professionals in India?
Quantiphi has large teams in Mumbai, Bengaluru. Openings and locations change often, so check the Quantiphi careers page and filter by city and experience.