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 with ESG data providers (MSCI, Sustainalytics, ISS), SFDR Article 8/9 classification, EU Taxonomy alignment, PAI indicators, and Scope 1-3 emissions or carbon intensity modelling.
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 datasets and methodologies, explains scoring divergence between providers, and computes portfolio carbon intensity or taxonomy alignment unaided.
02
Evaluation factor
Deals and deliverables that closed
25% weight
Probe actual outputs: sector ESG deep dives, engagement notes, exclusion list revisions, TCFD or ISSB reports, or fund screening decisions that changed a holding.
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
Points to named research notes, an issuer engagement they led, or a stewardship report that influenced a real allocation or divestment.
03
Evaluation factor
Risk judgement
25% weight
Test how they separate material ESG risk from noise: greenwashing red flags, controversy screening, stranded asset exposure, and where sustainability conflicts with return targets.
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
Uses SASB materiality logic, cites a company where headline ESG scores misled, and states trade-offs against mandate return objectives plainly.
04
Evaluation factor
Explaining it to decision-makers
15% weight
Assess how they brief portfolio managers, investment committees, and clients: proxy voting rationale, impact reporting to asset owners, and defending ratings challenged by an issuer.
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
Turns dense sustainability data into a clear recommendation with caveats, and holds a position under challenge from PMs or issuers.
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.
