Just finished. Wayfair Data Scientist's OA is surprisingly "friendly"!
The whole session was 105 minutes, with three questions: SQL + Pandas + Case Study, at a relatively relaxed pace, and of medium to low difficulty.
Here are the details straight away:
Question 1: SQL
Question.
Table. weather
Write a query to find the average temperature per city, and categorize the temperature level as cold, moderate, or hot using CASE WHEN.
This question is a very typical SQL interview question that focuses on AVG() + CASE WHEN The combined use of the
The common pitfalls are:
- forget about
GROUP BY city.; CASESyntax irregularities when writing multiconditionals in the- The order of output is not noted (sometimes required)
ORDER BY avg_temp DESC).
The overall difficulty is on the basic side, and anyone who has practiced LeetCode SQL can get a perfect score.
Question 2: Pandas
Question.
You are given a dataframe with several missing values in the column sales_amount. Fill the missing values with the median of each region instead of the global mean.
There's a little bit of detail in this question, and a lot of people are in the habit of using the mean()But the title emphasizes the use of median and the need to populate by groups.
The thought process is actually pretty straightforward:
df['sales_amount'] = df['sales_amount'].fillna(
df.groupby('region')['sales_amount'].transform('median')
)
The main points of the test are groupby + transform combinations, and inplace modifications.
It's not too difficult, but points are deducted for those stuck in median.
Question 3: Case Study
Case Topic.
Customer complaints data - reasons include Late Delivery, Product Damaged, and Customer Unsatisfied, with corresponding resolutions like Refund, Replacement, Apology.
Questions.
- Provide 3 insights from the data.
- Give 3 recommendations based on your insights.
- Choose the prioritized recommendation, explain how to measure its success, and what risks it might involve.
This question was the most thought-provoking of the entire session.
Common Thoughts:
- Insight: Which complaints are most frequent, which resolutions have the highest success rates, and whether there are regional or product differences.
- Recommendation: Improve the logistics process to reduce delays, optimize packaging to reduce damage, and customer service training to increase satisfaction.
- Prioritization: Prioritize the "Late Delivery" problem with the on-time delivery rate as a measurement indicator.
- Risk: Rising costs and reduced efficiency of inventory turnover.
The general idea is to put data → insight → recommendation → measurement → risk The links can be spoken smoothly without complex modeling.
Summarize
Wayfair DS OA is generally more "hands-on" than other companies, and the questions are much milder than other companies:
- SQL, Pandas are regular exams;
- Case questions are based on logic and expression.
For those who are preparing for the DS post, as long as they have practiced SQL/Pandas + basic case framing, this OA is really the type of OA where they can get high scores.
In conclusion.
This time, when I took a student to do Wayfair OA, we also remotely assisted him with voice assistance + real-time reminders, from SQL to Pandas, to help him stabilize the pace.
The case issue segment is particularly critical.Programhelp ‘s teacher will be in the background voice prompts you thought structure, such as "first speak INSIGHT, and then make up the RECOMMENDATION", to ensure that the answer is organized and clear, not stuck.
If you're going to take the test next, too Wayfair / Meta / Capital One / Instacart / Snowflake DS Post, can approach us in advance for a round of no-holds-barred simulations.
--Whether it is OA on-line writing, or VO interview assistance, can help you less step in the pit, quickly on shore.
