The interview will be 2 parts (for a total of ~30-45 minutes):
- Part 1: Case Analysis (10-20 minutes)
- Part 2: Technical Analysis (10-20 minutes)
For the technical portion of this interview, you can answer the questions in SQL, Python, or R, whichever language you feel most comfortable with; we do not have a preference! Note that several of our data processing questions are designed to be solved in SQL (with tables and columns as input), but we accept any programming language that can get to the solution.
Meta DS VO Detail
1. Introduce yourselves to each other
The first step is a self introduction part, Later, we entered the technical interview.
2. SQL – video call
Please provide the following two tables and write SQL statements. This question is not difficult, but it is not too difficult
table 1
callerid | recipientid | ds | call_id | duration
table 2
user_id | age_bucket | country | primary_os | dau_flag | ds
Question 1: How many users have started a call with more than 3 people in the last 7 days?
SELECT COUNT(DISTINCT callerid) AS user_count
FROM table1
WHERE ds >= DATE_SUB(CURRENT_DATE, INTERVAL 7 DAY)
GROUP BY callerid
HAVING COUNT(DISTINCT recipientid) > 3;Question 2: What percentage of daily active users from France (‘fr’) were on a video call yesterday?
WITH daily_active_users AS (
SELECT user_id
FROM table2
WHERE ds = DATE_SUB(CURRENT_DATE, INTERVAL 1 DAY)
AND country = 'fr'
AND dau_flag = 1
),
video_call_users AS (
SELECT DISTINCT callerid AS user_id
FROM table1
WHERE ds = DATE_SUB(CURRENT_DATE, INTERVAL 1 DAY)
UNION
SELECT DISTINCT recipientid AS user_id
FROM table1
WHERE ds = DATE_SUB(CURRENT_DATE, INTERVAL 1 DAY)
)
SELECT
COUNT(DISTINCT v.user_id) / COUNT(DISTINCT d.user_id) * 100 AS percentage_video_call_users
FROM daily_active_users d
LEFT JOIN video_call_users v ON d.user_id = v.user_id;3.Product Case
There are three types of notifications:
- Time critical (your friend is on live)
- Feedback (ask what’s your opinion on something)
- Security notification (password change)
Question 1: How to define the good quality of notification and what data do you need?
I mentioned the goal, and the goal is improvement engagement. Then, I discussed some high-level success metrics, such as DAU/MAU, time spent on the app, etc. Afterward, I talked about driver metrics, which are more actionable metrics, such as click-through rate (CTR) and whether users complete their intended actions after clicking. Finally, I covered some counter metrics, like opt-out rate and uninstall rate.
Question 2: How do you check if, let’s say, 35% CTR is good or not?
I mentioned that different types of notifications would have different benchmarks. Security-related notifications might have higher benchmarks. This section primarily involves interview content related to Product Case, focusing on the quality evaluation and measurement metrics of notifications. The core topics discussed include:
How to define a high-quality notification? (Evaluating from multiple dimensions, including engagement, success rate, CTR, opt-out rate, etc.)
How to determine if a 35% CTR is a good metric? (It should be compared against benchmarks for different notification types.)
Reference
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