Tiger Analytics R Programming Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Tiger Analytics hiring for R Programming roles
Tiger Analytics is an AI and analytics consulting company working with clients in consumer goods, retail, insurance, banking and healthcare. Its India offices handle data engineering, data science and ML engineering work. It hires for data analysts, data scientists, data engineers and ML engineers. Many projects run on major cloud data platforms.
For R Programming roles, this Tiger Analytics practice set focuses most on Modelling and Machine Learning in R (6), Functions and Functional Programming (6), Data Visualization with ggplot2 (5), Reports, Shiny and Production R (3). It has 5 easy, 21 medium, 4 hard questions, so you can start easy and work up to the harder ones.
Company type
Analytics
Headquarters
Santa Clara, California, USA
Main India offices
Chennai, Bengaluru, Hyderabad
Tiger Analytics R Programming interview process
Online assessment (SQL + Python + statistics)
Technical round 1
Technical round 2 or case round
Managerial round
HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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01
Your analysis gives slightly different results every time you run it. How do you make it reproducible?
Functions like sample(), rnorm() and random train-test splits depend on the random number generator. I call set.seed(42) at the start of the script so the same random sequence is generated every run, which lets reviewers reproduce results. For parallel code I use reproducible streams, for example future with seed = TRUE. I also record the R version, because the default sampling algorithm changed in R 3.6.0 and can change results between versions.
A confusion matrix compares actual and predicted classes: true positives, false positives, true negatives and false negatives. Accuracy is the share of correct predictions. Precision is TP / (TP + FP), how many predicted positives are correct. Recall is TP / (TP + FN), how many actual positives were found. F1 is their harmonic mean. For a loan default model, recall matters if missing defaulters is costly; precision matters if rejecting good customers is costly.
04
Your model has R-squared 0.92 on training data but 0.55 on test data. What do you do?
The gap indicates overfitting or data leakage. I first check for leakage, such as a feature computed using future information or the target itself, or duplicate records across train and test. Then I simplify the model, add regularization, reduce features or tune hyperparameters with cross-validation instead of a single split. If data is time-based, I make sure the split is chronological. I compare against a simple baseline to judge real improvement.
05
What does the ... (dots) argument do in an R function?
The dots let a function accept any number of additional arguments and pass them on to another function. For example, my_plot <- function(df, ...) ggplot(df, aes(x, y)) + geom_point(...) lets callers pass colour or size without declaring them. You can capture dots with list(...) or count them with ...length(). The downside is that misspelled argument names are silently ignored, so document them and validate with rlang::check_dots_used() in packages.
06
How do you do feature engineering with recipes in tidymodels?
A p-value is the probability of seeing results at least as extreme as the observed ones if the null hypothesis were true. A small p-value, typically below 0.05, means the data would be unusual under the null, so we reject it. It is not the probability that the null hypothesis is true and does not measure effect size. A tiny, unimportant difference can be significant with a huge sample, so always report effect sizes and confidence intervals too.
08
A loop calls an external API 5,000 times and crashes at call 3,200. How do you make it robust?
I wrap each call with purrr::safely() or tryCatch() so one failure does not stop the loop, and add retries with exponential backoff, which httr2 supports through req_retry(). I respect rate limits with Sys.sleep() or req_throttle(). I save results incrementally, for example every 100 calls to an RDS file or database, so a restart resumes from the last checkpoint. Finally I log failed IDs and rerun only those.
09
In your regression, coefficient signs look wrong and standard errors are huge. What might be the problem?
This is a classic sign of multicollinearity, where predictors are highly correlated, for example total spend and online spend. The model cannot separate their effects, so coefficients become unstable. I check correlations and the variance inflation factor with car::vif(); values above 5 or 10 are a concern. Solutions are removing or combining correlated variables, using principal components or ridge regression. Prediction accuracy may be fine, but interpretation is unreliable.
10
The same 40 lines of cleaning code are copied into ten scripts. What would you do?
I would extract the logic into well-named functions with arguments for the parts that differ, then move them into one shared file loaded with source() or, better, a small internal R package. A package gives documentation with roxygen2, tests with testthat and versioning, so every script uses the same tested logic. Fixing a bug then happens in one place. I would migrate the scripts gradually and compare outputs to confirm nothing changed.
Base R stores dates as the Date class, counting days since 1970-01-01, and date-times as POSIXct, counting seconds. as.Date('2026-03-15') parses ISO strings and format() controls output. The lubridate package simplifies parsing with ymd(), dmy() and mdy(), extracting parts with month() and wday(), and arithmetic like date + months(1). Time zones matter: Indian data usually uses 'Asia/Kolkata', and mixing UTC with IST can shift records across days.
12
Product wants to detect a lift in conversion from 5 to 5.5 percent. How many users do you need per group?
I use power analysis before the test starts. In R, power.prop.test(p1 = 0.05, p2 = 0.055, power = 0.8, sig.level = 0.05) gives roughly 31,000 users per group. Smaller lifts need far larger samples, since sample size scales with the inverse square of the effect. I would then convert the sample into test duration using daily traffic, round up to full weeks, and agree on the stopping rule in advance to avoid peeking bias.
13
Your team wants to share a Shiny app with 200 internal users. What deployment options exist?
Options include shinyapps.io, a hosted service that is quickest to start but may not suit sensitive internal data, Posit Connect, a commercial on-premise or cloud server with authentication, scheduling and scaling, the free open-source Shiny Server on a Linux machine, and a Docker container behind a reverse proxy like Nginx, often with ShinyProxy for authentication. For internal data, I would choose Connect or containers inside the company network with single sign-on.
14
What is a pure function and why prefer them in data pipelines?
A pure function always returns the same output for the same inputs and has no side effects, such as modifying global variables, writing files or printing. Pure functions are easy to test, reuse, cache and run in parallel, because they do not depend on hidden state. In data pipelines, I keep transformations pure, taking a data frame and returning a new one, and isolate side effects such as reading and writing in a few clearly named functions.
15
How do you build a logistic regression model for churn in R?
ggsave() saves the last plot or a named plot with exact size and resolution.
p <- ggplot(df, aes(city, sales)) + geom_col()
ggsave('output/sales_by_city.png', plot = p,
width = 8, height = 5, dpi = 300)
ggsave('output/sales_by_city.pdf', plot = p,
width = 8, height = 5) # vector, sharp at any zoom
# svg for web use
ggsave('output/sales_by_city.svg', plot = p)
17
Write ggplot2 code for a monthly sales line chart with points.
Dual-axis charts plot two measures with different scales on one chart, and the visual relationship between the lines depends on arbitrary axis ranges, so readers can be misled about correlation. ggplot2 only allows a secondary axis that is a one-to-one transformation of the primary axis through sec_axis(), such as Celsius and Fahrenheit. For two different metrics, I prefer two aligned panels using facets or the patchwork package.
19
How would you compare R and Python for a data role?
R is strongest for statistics, exploratory analysis, visualization and reproducible reporting, with mature packages for biostatistics, econometrics and survey analysis. Python is a general-purpose language that is stronger for production software, deep learning and web integration. Many teams use both: R for analysis and reporting, Python for pipelines and deployment. In an interview, avoid language wars and explain that the choice depends on the team's stack, the problem and who maintains the code.
20
You are building a fraud model where only 0.5 percent of transactions are fraud. How do you handle the imbalance?
Accuracy is useless here, since predicting no fraud gives 99.5 percent. I use stratified splits and evaluate with precision, recall, F1 and the precision-recall AUC. To help the model learn the minority class, I try class weights, downsampling the majority or SMOTE through the themis package in a recipe, applied only to training data. Then I tune the probability threshold based on the cost of missed fraud versus false alarms agreed with the business.
21
How do you add labels and annotations to a ggplot chart?
Situation: while preparing a monthly revenue dashboard, totals were 8 percent higher than finance figures. Task: find the cause before the review meeting. Action: I traced row counts after each step in my R script and found a join on customer ID that duplicated rows because some customers had two active addresses. I deduplicated the address table and added a row-count check. Result: numbers matched finance, and the check now runs in every refresh.
24
Your function fails with 'the condition has length > 1' when given a vector. What is wrong?
An if statement can only check a single TRUE or FALSE value, and since R 4.2 a longer condition is an error. The function was written for one value but received a vector. I would replace if with a vectorized alternative such as ifelse(), dplyr::if_else() or case_when(). If the function is truly meant for single values, I validate the input length with stopifnot(length(x) == 1) or apply it over elements with purrr::map().
25
Tell me about the messiest dataset you have cleaned.
Situation: I received three years of vendor invoices from different branches in Excel files with inconsistent headers, merged cells and dates in four formats. Task: build one clean table for spend analysis. Action: I used readxl to read all sheets, janitor::clean_names() to standardise headers, lubridate to parse dates and a mapping table for vendor name variants. I wrote validation checks for totals. Result: a clean dataset of 180,000 rows that finance now refreshes monthly.
26
You notice database passwords hardcoded in a shared R script. What do you do?
I remove them immediately, inform the owner and ask for the password to be rotated, since it may exist in version history. Then I store credentials outside the code, in a user-level .Renviron file read with Sys.getenv(), the keyring package backed by the operating system's credential store, or the server's secret management, such as environment variables in Posit Connect. I make sure .Renviron is listed in .gitignore and scan the repository history.
27
Tell me about feedback you received in a code review and what you changed.
Situation: in my first quarter, a senior reviewed my R reporting script. Task: address the review comments. Action: the main feedback was that my 300-line script mixed data loading, cleaning and plotting with repeated code. I split it into small functions, added input checks and moved constants to a config file. Result: the next report took half the time to build, and I now structure every new script this way from the start.
28
How do you split one column into several, or combine columns into one?
geom_smooth() adds a fitted trend line with a confidence band, using loess for small data and a GAM for large data by default, or method = 'lm' for a straight line. stat_summary() computes summaries of y for each x, for example stat_summary(fun = mean, geom = 'point') to plot group means, or fun.data = mean_cl_normal for error bars. Both let you show patterns without precomputing summaries.
30
You are analysing a national survey where some groups were deliberately oversampled. How do you compute correct averages?
Simple averages would be biased toward oversampled groups, so I use the survey weights provided with the data. In R, the survey package defines the design with svydesign(ids = ~psu, strata = ~stratum, weights = ~wt, data = df), then svymean(), svytotal() and svyglm() give weighted estimates with correct standard errors that account for clustering and stratification. The srvyr package offers the same with dplyr-style syntax.
Tips to crack the Tiger Analytics R Programming interview
Revise machine learning algorithms, feature engineering and evaluation metrics, as technical rounds often go into depth.
Practise PySpark and SQL for data engineering roles, including data pipeline design on cloud platforms.
Prepare analytics case questions on demand forecasting or customer segmentation for retail and consumer goods clients.
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; Tiger Analytics interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Tiger Analytics R Programming interview?
Candidates usually go through 5 stages: Online assessment (SQL + Python + statistics); Technical round 1; Technical round 2 or case round; Managerial round; 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 Tiger Analytics R Programming interview?
This practice set leans on Modelling and Machine Learning in R (6), Functions and Functional Programming (6), Data Visualization with ggplot2 (5), Reports, Shiny and Production R (3). Revise those first, then practise explaining one or two of your own projects end to end, because most Tiger Analytics interviewers ask you to walk through real work.
Are these the exact questions asked at Tiger Analytics?
No. These are curated practice questions that match the skills and difficulty usually tested in analytics interviews like Tiger Analytics's. They are not a leaked or verified list of past questions.
Where does Tiger Analytics hire R Programming professionals in India?
Tiger Analytics has large teams in Chennai, Bengaluru, Hyderabad. Openings and locations change often, so check the Tiger Analytics careers page and filter by city and experience.