Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.
01
Evaluation factor
Technical command
35% weight
Check fluency in on-chain data work: Dune or Nansen queries, Glassnode metrics, wallet clustering, token supply schedules, staking yields, and how they value an L1 or DeFi protocol.
Evidence to listen for
Commands the instruments, models, or reporting standards the role turns on
Can build the analysis rather than only interpret someone else's
Knows the assumptions inside a model and which ones actually drive the answer
Fluent in the frameworks and disclosure regimes that apply
Five-point scoring guide
1
Poor
Cannot explain the instruments or standards they claim to work with.
2
Needs Improvement
Interprets others' analysis but cannot build or defend it.
3
Satisfactory
Solid working command; thin on unfamiliar structures or standards.
4
Very Good
Builds the analysis and knows which assumptions actually move the answer.
5
Excellent
Names specific queries, metrics (MVRV, TVL, realised cap) and valuation frames, explaining why each fits a given token.
02
Evaluation factor
Deals and deliverables that closed
25% weight
Ask for published output: token due diligence memos, protocol coverage notes, treasury allocation recommendations, exchange listing reviews, or backtested strategies, and what decision each one drove.
Evidence to listen for
Names transactions, filings, or reports they worked, with size, counterparties, and their own scope
Distinguishes their contribution from the deal team's
Knows what happened afterwards, including what underperformed
Can describe one that fell over and why
Five-point scoring guide
1
Poor
No completed work; describes process rather than outcomes.
2
Needs Improvement
Involved in transactions but cannot state their own scope.
3
Satisfactory
Real deliverables with adequate ownership; outcomes described loosely.
4
Very Good
Named transactions or filings with clear personal scope and honest post-mortems.
5
Excellent
Cites dated reports or theses with the position taken, sizing or listing outcome, and how the call actually performed.
03
Evaluation factor
Risk judgement
25% weight
Probe judgement on smart contract risk, bridge and custody exposure, stablecoin depegs, liquidity depth, unlock cliffs, and counterparty failures like FTX or Celsius.
Evidence to listen for
Distinguishes a modelled risk from a real one
States confidence and what would change their view
Comfortable disagreeing with a number that suits everybody
Knows the limits of the data behind a projection, especially over long horizons
Five-point scoring guide
1
Poor
Treats model output as truth; no sense of data limits.
2
Needs Improvement
Reports numbers without qualifying them; avoids unwelcome conclusions.
3
Satisfactory
Reasonable judgement; qualifies findings when prompted.
4
Very Good
States confidence unprompted and will hold an unpopular position on evidence.
5
Excellent
Describes a risk they flagged early, the on-chain or governance signal behind it, and the exposure it avoided.
04
Evaluation factor
Explaining it to decision-makers
15% weight
Test how they brief non-crypto stakeholders: portfolio managers, compliance, or the board on MiCA, Travel Rule, custody structures, and volatility without jargon.
Evidence to listen for
Explains a technical position to an investment committee, board, or regulator so they can act on it
Writes to the standard the audience is held to
Handles challenge without either caving or digging in
Works across legal, operations, and external counterparties
Five-point scoring guide
1
Poor
Cannot communicate beyond technical peers.
2
Needs Improvement
Explanations lose the audience or oversimplify to the point of error.
3
Satisfactory
Adequate with familiar audiences; less effective under challenge.
4
Very Good
Explains clearly to committees and regulators and holds up under challenge.
5
Excellent
Translates on-chain evidence into a plain recommendation with stated confidence, key assumptions, and what would change their view.
Put this rubric to work
Score every candidate against the same standard
Add these weighted factors to Hirevire and let AI evaluate recorded answers against your rubric.
