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
Probe LCA methodology, the standards they work to, impact categories, and the databases and software they use.
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
Builds studies to the applicable standards themselves, and knows which assumptions actually drive each result.
02
Evaluation factor
Deals and deliverables that closed
25% weight
Look for studies that were critically reviewed or published, and what the reviewers challenged.
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
Names reviewed or published studies they authored, including what a critical reviewer pushed back on.
03
Evaluation factor
Risk judgement
25% weight
Test how they handle system boundaries, allocation, and data gaps where a defensible choice changes the headline.
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
Makes boundary and allocation choices transparently and runs sensitivity analysis rather than picking the flattering one.
04
Evaluation factor
Explaining it to decision-makers
15% weight
Assess how they present a result that undermines a product claim marketing has already made.
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
Holds a defensible result against commercial pressure and explains the caveats so nobody overclaims from it.
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