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Molocoads·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
Apr 2026

Summary

SQL-heavy screen for a data scientist role at Molocoads. Two questions, both involving multi-table joins and window functions. Nothing behavioral, just pure query writing under time pressure.

Questions Asked (2)

Q1

Given a table of daily ad spend by advertiser, find the top 3 spending advertisers for each day. Handle ties so that advertisers tied within rank 3 are all included. Return the date, advertiser ID, their total daily spend, and their rank.

Product Analytics & MetricsData Modeling
Author's notes

This is a DENSE_RANK vs ROW_NUMBER question in disguise.

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AI HintsAI Generated

Suggested Approach

Use a window function like DENSE_RANK() partitioned by date and ordered by total daily spend descending to assign ranks. Then filter to keep only rows where rank <= 3, which naturally includes all ties at rank 3. Finally, select the date, advertiser ID, total spend, and rank.

Pro tip: Clarify whether 'top 3' means exactly three advertisers or all advertisers tied at the third rank; using DENSE_RANK ensures ties are handled correctly without arbitrarily cutting off tied advertisers.

1. Aggregate daily spend per advertiser

Group the data by date and advertiser ID, summing the spend to get total daily spend for each advertiser.

2. Rank advertisers within each day

Apply a window function such as DENSE_RANK() over a partition by date, ordered by total daily spend descending.

3. Filter to top 3 ranks

Keep only rows where the rank is less than or equal to 3, ensuring all tied advertisers at rank 3 are included.

4. Select and order final output

Return the date, advertiser ID, total daily spend, and rank, ordered by date and rank for readability.

Key Points to Mention

  • Use of window functions like DENSE_RANK() or RANK() to handle ties appropriately.
  • Partitioning by date to compute ranks independently for each day.
  • Aggregation of spend before ranking to get total daily spend per advertiser.
  • Filtering condition rank <= 3 to include all tied advertisers at the third position.
  • Consideration of performance and indexing for large datasets.
  • Clarifying business definition of 'top 3' in the presence of ties.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Q2

For each campaign goal type, calculate the total spend, total KPI conversions, and cost per KPI. Only include spend and conversions that occurred on or after the campaign's launch date. Return NULL for cost per KPI if there were zero conversions.

Data ModelingProduct Analytics & Metrics
Author's notes

Trickier than it looks because you have three tables and the launch date filter has to happen at the row level before aggregating, not after.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the data model and the definition of a campaign goal type, then outline a SQL query that joins campaign metadata with spend and conversion events, filtering by launch date. Use conditional aggregation to compute totals per goal type, and handle division by zero with a NULLIF or CASE statement to return NULL for cost per KPI when conversions are zero.

Pro tip: Always confirm whether 'campaign goal type' is a column in the campaigns table or requires a join to a goals dimension, and check if spend and conversions are in separate tables—this avoids incorrect assumptions and ensures accurate aggregation.

1. Clarify requirements and data model

Ask about the schema: how campaign goals, spend, and conversions are stored, and whether launch date is per campaign or per goal. Confirm that 'on or after launch date' applies to both spend and conversions.

2. Identify relevant tables and join keys

Determine the tables for campaigns (with goal type and launch date), spend, and conversions. Plan joins on campaign ID and ensure date filters are applied correctly.

3. Filter and aggregate data

Filter spend and conversion records to those on or after the campaign's launch date. Then aggregate total spend and total conversions per campaign goal type using SUM and COUNT or SUM of conversion flags.

4. Compute cost per KPI with NULL handling

Calculate cost per KPI as total spend divided by total conversions, using a CASE statement or NULLIF to return NULL when conversions are zero. Ensure the result is grouped by goal type.

5. Validate and present results

Sanity-check results: ensure no negative costs, verify totals match expectations, and format output clearly. Mention potential edge cases like missing spend or conversions.

Key Points to Mention

  • Use of LEFT JOINs to include campaigns with zero spend or conversions, ensuring all goal types are represented.
  • Application of date filters (>= launch_date) on both spend and conversion records to meet the 'on or after' condition.
  • Conditional aggregation with SUM(CASE WHEN ...) or COUNT to compute totals per goal type.
  • Handling division by zero using NULLIF or CASE to return NULL for cost per KPI when conversions = 0.
  • Grouping by campaign goal type and ensuring the output includes all goal types, even those with no activity.
  • Consideration of data granularity: whether spend and conversions are at campaign level or more granular, and if pre-aggregation is needed.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.