Find Candidates for Data Scientist Position
A easy-tier problem at 76% community acceptance, tagged with Database. Reported in interviews at HashedIn and 0 others.
You're prepping for a HashedIn assessment and just saw a database query problem labeled 'Easy' with a 76% acceptance rate. Don't let the label fool you. This one trips people up because it looks like a straightforward SELECT but requires you to think about schema design, filtering logic, and how to actually validate candidates against multiple criteria. The problem teaches you how to write SQL that scales, something you'll use on the job. If you hit this during your live assessment and blank on the join or WHERE clause logic, StealthCoder surfaces a working solution in seconds, invisible to the proctor.
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Find Candidates for Data Scientist Position is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Built because the OA filter rejects engineers who'd pass the on-site. That's a broken filter. This is the workaround.
Get StealthCoderThis problem tests whether you can structure a SQL query that correctly filters and returns data based on business logic. The trap is assuming a simple WHERE clause is enough. Most candidates either over-join tables, misunderstand the filtering criteria, or write inefficient subqueries. The real skill here is reading the requirements carefully: which columns matter, how do you exclude invalid rows, and does order matter. Database problems at this tier are deceptively easy to bungle because SQL syntax doesn't always tell you when your logic is wrong, you get results, just the wrong ones. You need to validate your output against the spec manually. StealthCoder is the hedge for the one query you didn't test thoroughly before submitting.
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You know the problem.
Make sure you actually pass it.
Find Candidates for Data Scientist Position recycles across companies for a reason. It's easy-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Built because the OA filter rejects engineers who'd pass the on-site. That's a broken filter. This is the workaround. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Find Candidates for Data Scientist Position interview FAQ
Is this problem still asked at HashedIn?+
Yes. It's in their reported assessment rotation for data science roles. The phrasing and schema may vary slightly, but the core pattern, filtering candidates by multiple attributes, is a recurring theme in their SQL screening.
What's the most common mistake on this one?+
Candidates join tables but don't filter correctly, or they filter on the wrong column. Read the requirements three times. Make sure you know what 'candidate' actually means in the schema and what disqualifies someone. Test your query against example data before submitting.
How does this relate to real data science work?+
This is literally what you do daily: pull subsets of data that meet business criteria. A hiring manager or stakeholder asks 'give me all candidates who match X, Y, Z.' Your SQL needs to be correct and fast. This problem teaches you to write that query without mistakes.
Do I need to optimize this for performance?+
At the Easy tier, correct output matters more than optimization. Write a clean, readable query first. If it passes, great. Optimization (indexes, execution plans) comes later. Don't over-engineer on your first pass.
Is there a trick to the filtering logic?+
Not really a trick. Just be precise: AND vs OR in your WHERE clause changes everything. Map each requirement to a condition. If the spec says 'candidates with skill A OR skill B', use OR. If it says 'must have both', use AND. Read twice, code once.
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