Interview scorecard template

Climate Risk Insurance Specialist interview scorecard

Evaluate Climate Risk Insurance Specialist candidates across 4 weighted areas: technical command, deals and deliverables that closed, risk judgement, and explaining it to decision-makers. Technical command leads at 35%, so check fluency with catastrophe models (RMS, Verisk/AIR, Oasis LMF), CMIP6 downscaling, RCP/SSP pathways, exceedance probability curves, and how they adjust vendor event. Use the rubric to compare role-specific evidence consistently.

See AI scoring
finance riskcatastrophe modellingphysical climate riskreinsurance pricingtcfd disclosure
TL;DR
For technical command, look for evidence the candidate names specific model versions and perils, explains secondary uncertainty and demand surge handling, and where vendor views understate flood or wildfire. For deals and deliverables that closed, look for evidence the candidate cites named portfolios with premium, exposure or loss ratio movement, and the regulator, reinsurer or board that accepted the work. Apply the written 1–5 anchors to every answer, record the evidence behind each rating, and use the factor weights to reach a consistent overall assessment.
Complete evaluation framework

What to assess and how to score it

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 catastrophe models (RMS, Verisk/AIR, Oasis LMF), CMIP6 downscaling, RCP/SSP pathways, exceedance probability curves, and how they adjust vendor event sets for climate signal.

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 model versions and perils, explains secondary uncertainty and demand surge handling, and where vendor views understate flood or wildfire.

02
Evaluation factor

Deals and deliverables that closed

25% weight

Probe deliverables that shipped: repriced wildfire or coastal flood portfolios, ORSA climate scenario chapters, TCFD or ISSB disclosures, reinsurance submissions, or Solvency II internal model change files.

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 named portfolios with premium, exposure or loss ratio movement, and the regulator, reinsurer or board that accepted the work.

03
Evaluation factor

Risk judgement

25% weight

Test how they set risk appetite where data is thin: unmodelled perils, accumulation limits by CRESTA zone, attachment point choices, and refusing or surcharging exposures under political pressure.

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

Distinguishes model uncertainty from genuine trend, states thresholds triggering withdrawal or subsidised cover, and owns a call that proved wrong.

04
Evaluation factor

Explaining it to decision-makers

15% weight

Assess how they brief underwriters, actuaries and boards: translating return periods and average annual loss into pricing and capital decisions without hiding tail assumptions.

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 EP curves into concrete underwriting guidance, surfaces key assumptions plainly, and has changed an executive decision with a single clear exhibit.

Evidence-led prompts

Interview questions for a Climate Risk Insurance Specialist

Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.

  1. 01

    Can you describe your experience with climate risk modelling and assessment?

  2. 02

    Can you describe a complex project you led in this area?

  3. 03

    Can you give an example of a risk mitigation strategy you implemented?

  4. 04

    What methods do you use to forecast climate-related losses?

  5. 05

    How do you assess physical and transition risks separately?

See the complete Climate Risk Insurance Specialist question set
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.

Explore AI Scorecards