Is Find Candidates for Data Scientist Position II easy or hard?

The problem is rated Medium because it combines multiple SQL concepts. You must correctly join tables, compute grouped statistics, and sometimes apply window functions to filter or rank candidates based on evaluation metrics.

Find Candidates for Data Scientist Position II Python/Java solution

This is a database problem, so the primary solution is written in SQL rather than Python or Java. In practice, the query would be executed through a Python or Java database client, but the core logic remains a single SQL query using joins, aggregation, and window functions.

How to solve Find Candidates for Data Scientist Position II in O(n)?

Pure O(n) complexity is difficult because SQL engines usually sort data during GROUP BY or window operations. The closest approach is performing a single pass join followed by aggregation with indexed columns, which reduces overhead but still behaves closer to O(n log n) in practice.

What is the best approach for Find Candidates for Data Scientist Position II?

The most practical solution uses an equi-join to combine candidate records with related evaluation data, followed by GROUP BY aggregation to compute statistics per candidate. A window function such as RANK() or ROW_NUMBER() can then rank or filter candidates based on those aggregated metrics. This approach keeps the query readable while handling complex evaluation logic.

Is Find Candidates for Data Scientist Position II asked at Google/Amazon/Meta?

Problems involving SQL joins, aggregation, and window functions frequently appear in interviews at data-driven companies such as Google, Amazon, and Meta. Variants of candidate filtering or ranking queries are common in data engineering and analytics interviews.

What data structure is used in Find Candidates for Data Scientist Position II?

The problem relies on relational database structures and SQL operations rather than traditional in-memory data structures. Key mechanisms include equi-joins for combining tables, GROUP BY aggregations for statistics, and window functions for ranking or comparison.

What is the time complexity of Find Candidates for Data Scientist Position II?

Most SQL solutions run in O(n log n) time due to grouping and sorting operations required for aggregation and window functions. The space complexity is typically O(n) because intermediate grouped results or derived tables store per-candidate statistics.

3278. Find Candidates for Data Scientist Position II - Practice Online

Table: Candidates

+--------------+---------+ 
| Column Name  | Type    | 
+--------------+---------+ 
| candidate_id | int     | 
| skill        | varchar |
| proficiency  | int     |
+--------------+---------+
(candidate_id, skill) is the unique key for this table.
Each row includes candidate_id, skill, and proficiency level (1-5).

Table: Projects

+--------------+---------+ 
| Column Name  | Type    | 
+--------------+---------+ 
| project_id   | int     | 
| skill        | varchar |
| importance   | int     |
+--------------+---------+
(project_id, skill) is the primary key for this table.
Each row includes project_id, required skill, and its importance (1-5) for the project.

Leetcode is staffing for multiple data science projects. Write a solution to find the best candidate for each project based on the following criteria:

Candidates must have all the skills required for a project.
Calculate a score for each candidate-project pair as follows:
- Start with 100 points
- Add 10 points for each skill where proficiency > importance
- Subtract 5 points for each skill where proficiency < importance
- If the candidate's skill proficiency equal to the project's skill importance, the score remains unchanged

Include only the top candidate (highest score) for each project. If there’s a tie, choose the candidate with the lower candidate_id. If there is no suitable candidate for a project, do not return that project.

Return a result table ordered by project_id in ascending order.

The result format is in the following example.

Example:

Input:

Candidates table:

+--------------+-----------+-------------+
| candidate_id | skill     | proficiency |
+--------------+-----------+-------------+
| 101          | Python    | 5           |
| 101          | Tableau   | 3           |
| 101          | PostgreSQL| 4           |
| 101          | TensorFlow| 2           |
| 102          | Python    | 4           |
| 102          | Tableau   | 5           |
| 102          | PostgreSQL| 4           |
| 102          | R         | 4           |
| 103          | Python    | 3           |
| 103          | Tableau   | 5           |
| 103          | PostgreSQL| 5           |
| 103          | Spark     | 4           |
+--------------+-----------+-------------+

Projects table:

+-------------+-----------+------------+
| project_id  | skill     | importance |
+-------------+-----------+------------+
| 501         | Python    | 4          |
| 501         | Tableau   | 3          |
| 501         | PostgreSQL| 5          |
| 502         | Python    | 3          |
| 502         | Tableau   | 4          |
| 502         | R         | 2          |
+-------------+-----------+------------+

Output:

+-------------+--------------+-------+
| project_id  | candidate_id | score |
+-------------+--------------+-------+
| 501         | 101          | 105   |
| 502         | 102          | 130   |
+-------------+--------------+-------+

Explanation:

For Project 501, Candidate 101 has the highest score of 105. All other candidates have the same score but Candidate 101 has the lowest candidate_id among them.
For Project 502, Candidate 102 has the highest score of 130.

The output table is ordered by project_id in ascending order.