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ML Academy

S22 · interview simulation

Committee review of a simulated analyst candidate pack

People, data and outputs in this scenario are simulated. Each stage is recorded when you commit; the reveal comes after.

  • 4stages
  • 40 minabout
  • 0stages committed

The brief

You are the data analytics lead on a simulated hiring committee. The candidate (call them K, a synthetic person created for this exercise) applies for a Data Analyst role. Their pack contains a competency scorecard built under the academy's scoring rules and three artefacts.

The scoring rules you apply: a dimension needs at least 3 distinct items and total weight of at least 2, otherwise it shows insufficient evidence (never 0) and is excluded from averages. Role readiness is the weighted mean over dimensions with estimates, reported only if those dimensions cover at least half of the role's weight. Strengths are scores of 75 or more and concerns below 60. The role weights below are hypothetical for this exercise.

Stage 1 of 4

DimensionRole weightScoreItemsTotal evidence weight
SQL and analytical modeling0.2582149.8
Visualization and storytelling0.2078118.1
Statistics and experiments0.205896.0
EDA and data quality0.157164.0
ML fundamentals0.106453.5
Causal reasoning0.05not scored21.0
Forecasting0.05not scored10.5

What is K's role readiness for Data Analyst, and how do you report it?

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