Interview scorecard template

Earthquake Early Warning System Specialist interview scorecard

Evaluate Earthquake Early Warning System Specialist candidates across 4 weighted areas: technical depth, work that shipped, diagnosis under uncertainty, and working across the org. Technical depth leads at 35%, so probe depth in real-time seismology: P-wave picking, magnitude estimators such as ElarmS, FinDer or PLUM, ground-motion prediction equations, MMI and PGA. Use the rubric to compare role-specific evidence consistently.

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engineering applied sciencereal time alertingseismic networksseismologyshakealert
TL;DR
For technical depth, look for evidence the candidate explains algorithm trade-offs between point-source and finite-fault estimators, and quotes realistic latency and magnitude error figures from operational data. For work that shipped, look for evidence the candidate names specific networks, stations or alert integrations they delivered, with alert times, false-alert rates and downstream users served. 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 depth

35% weight

Probe depth in real-time seismology: P-wave picking, magnitude estimators such as ElarmS, FinDer or PLUM, ground-motion prediction equations, MMI and PGA thresholds, station telemetry latency budgets.

Evidence to listen for

  • Explains the physics or mechanism behind their work, not just the tooling
  • Names the standards, tolerances, and constraints they designed against
  • Can defend a design decision under follow-up questions
  • Distinguishes what they personally engineered from what the team delivered

Five-point scoring guide

1
Poor

Cannot explain the fundamentals of their own stated specialism.

2
Needs Improvement

Knows the vocabulary but not the underlying mechanism; struggles under follow-ups.

3
Satisfactory

Solid working knowledge for the role; depth thins out on edge cases.

4
Very Good

Strong command of the domain; explains trade-offs and defends decisions well.

5
Excellent

Explains algorithm trade-offs between point-source and finite-fault estimators, and quotes realistic latency and magnitude error figures from operational data.

02
Evaluation factor

Work that shipped

30% weight

Ask what they built or ran: ShakeAlert or JMA style pipelines, SeisComP or Earthworm deployments, broadband and strong-motion station installs, alert delivery to transit or utility clients.

Evidence to listen for

  • Names specific programmes, parts, or systems that reached production or field use
  • States their own scope inside the project
  • Can give measured outcomes: yield, cycle time, cost, failure rate
  • Explains what went wrong and what they changed

Five-point scoring guide

1
Poor

No delivered work; experience is coursework, lab-only, or purely observational.

2
Needs Improvement

Contributed to projects but cannot say what shipped or what their part was.

3
Satisfactory

Has delivered real work; outcomes described without numbers.

4
Very Good

Names shipped work and their scope, with some measured results.

5
Excellent

Names specific networks, stations or alert integrations they delivered, with alert times, false-alert rates and downstream users served.

03
Evaluation factor

Diagnosis under uncertainty

20% weight

Test how they handled missed or false alerts: telemetry dropouts, clipped channels, teleseismic contamination, blast or quarry signals, offshore events with poor azimuthal coverage.

Evidence to listen for

  • Describes a real failure they chased to root cause
  • Shows a method: isolate variables, reproduce, measure, eliminate
  • Distinguishes correlation from cause
  • Says what they ruled out and why, not only what the answer turned out to be

Five-point scoring guide

1
Poor

No diagnostic method; guesses or escalates immediately.

2
Needs Improvement

Trial and error with no structure; cannot explain how they narrowed the cause.

3
Satisfactory

Reasonable method on familiar problems; less structured on novel ones.

4
Very Good

Clear systematic approach with a real root-cause story.

5
Excellent

Walks through a real false or late alert, the waveform evidence examined, and the tuning or station change that fixed it.

04
Evaluation factor

Working across the org

15% weight

Look for work with emergency managers, rail and utility operators, IT teams on alert distribution (CAP, WEA) and public education on seconds of warning.

Evidence to listen for

  • Explains technical constraints to non-technical stakeholders without condescension
  • Has negotiated scope, cost, or timeline with manufacturing, product, or suppliers
  • Documents decisions so others can act on them
  • Takes review feedback without defensiveness

Five-point scoring guide

1
Poor

Cannot communicate outside their specialism; dismissive of other functions.

2
Needs Improvement

Communication gaps cause rework; avoids stakeholder contact.

3
Satisfactory

Works adequately with other teams; documentation is thin.

4
Very Good

Communicates clearly across functions; reliable collaborator.

5
Excellent

Describes translating warning uncertainty into usable protective actions for operators, and negotiating thresholds with non-seismologist stakeholders.

Evidence-led prompts

Interview questions for a Earthquake Early Warning System Specialist

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

  1. 01

    What experience do you have developing or managing early warning systems for natural hazards?

  2. 02

    Can you describe a time when you implemented an earthquake early warning solution?

  3. 03

    Describe a situation where an early warning system helped reduce damage or protect people.

  4. 04

    How do you handle false positives and false negatives in detection and warning?

  5. 05

    How do you ensure the accuracy and reliability of your detection models?

See the complete Earthquake Early Warning System Specialist question set
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