Field ops, grid, sustainability

Hiring for energy? Here's how we evaluate.

We score how candidates reason through a field incident and communicate risk under real constraints — not whether they can recite the regulation number. The model runs the scenario; the rubric is ours.

See the rubric

What we score

The dimensions, not the playbook.

We don't publish the exact criteria, weights, or sub-probes — that's how candidates would game the rubric. Here's what every energy candidate is scored against.

Systems reasoning under constraint
Whether they reason through a fault or outage with the actual constraints of the field — limited visibility, competing signals, real time pressure — or answer as if working from a clean diagram.
Regulatory and safety judgment
Whether they flag a compliance or safety concern even when it slows things down, or treat it as a box to check after the fact. Scored independently of technical correctness.
Incident communication
How clearly they report status up the chain and out to affected stakeholders — utilities, regulators, or customers each need a different level of detail and a different tone.
Trade-off transparency
Whether they're upfront about what they don't know yet in a fast-moving incident, or fill the gap with confident-sounding guesses. We score the honesty of the uncertainty, not just the final call.

Sample scenarios

What candidates actually face.

Two illustrative scenario types — the actual prompts vary per session and stay private to your tenant.

Scenario 1
A field fault with incomplete telemetry.
The AI plays a control-room contact relaying partial, sometimes conflicting readings. We score whether the candidate reasons carefully through the gap or commits to a diagnosis too early.
Scenario 2
A stakeholder update during an active incident.
Mid-scenario, the candidate has to explain status to someone outside their team — a regulator, a customer, a non-technical manager. We score clarity and honesty about what's still unresolved, not polish.

Integrity signals

What we watch for — and what stays private.

We name the signals we capture, but not how we weight or threshold them. That's the part that breaks if we publish it.

  • Every session is recorded — audio, video, and full transcript — and retained per your tenant policy.
  • Every score ships with an ML confidence band. Low-confidence scores are flagged for human review before the candidate is decided on.
  • We evaluate judgment and communication signals only — SkillPlatform doesn't certify field qualifications, safety training, or regulatory credentials. Those stay with your existing certification process.
  • Admin labeling lets your team flag interviews where the AI's read of a scenario diverged from what a senior field lead would catch.
  • We never train shared models on your candidate data.

What we measure

The outcome you can defend.

Systems-reasoning score, safety-judgment score, and end-to-end completion rate for every candidate — plus a confidence band on each. We measure how often our 'strong hire' candidates clear your field or ops panel, and we recalibrate when the gap widens. The metric that matters most: the rate at which our 'no hire' signal earns enough trust to skip a full second-round screen.

We frame these as what we measure, not as customer-attributed metrics.

Want to see how this rubric scores a real candidate?

An expert will walk you through a live energy interview transcript — including how the integrity signals played out — in 15 minutes.

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Other domains:EngineeringHealthcareManufacturing
SOC 2 Type II — In progressGDPR-readyTenant-isolated infrastructureData residency: USOngoing rubric consistency reviewNo training on your candidate data