software datagis modelingintelligent transportation systemsprobe datatraffic signal analytics
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 proficiency
35% weight
Check hands-on command of traffic data tooling: Python or R with pandas, SQL against ATSPM or detector archives, ArcGIS or QGIS, Synchro, Vissim, GTFS feeds, INRIX or HERE probe data.
Evidence to listen for
Command of the languages, frameworks, and data tools the role actually uses
Understands correctness, performance, and failure modes, not just syntax
Has opinions on testing and can justify them
Reads and reasons about code they did not write
Five-point scoring guide
1
Poor
Cannot work independently; fundamentals are missing.
2
Needs Improvement
Weak fundamentals; output needs heavy review.
3
Satisfactory
Competent for the role; needs guidance on complex or unfamiliar work.
4
Very Good
Strong practitioner; handles hard problems with little guidance.
5
Excellent
Names specific pipelines they built, joins probe speeds to signal event logs, and explains volume, occupancy, and travel time index calculations without hedging.
02
Evaluation factor
Systems and trade-offs
25% weight
Probe how they handle messy field data: failed loop detectors, gaps in Bluetooth re-identification, sample bias in probe coverage, and choosing corridor simulation over simpler before-after analysis.
Evidence to listen for
Reasons about scale, latency, cost, and failure before writing code
Names the trade-off they chose and what they gave up
Understands the data lifecycle end to end
Anticipates what breaks at ten times the volume
Five-point scoring guide
1
Poor
No thinking beyond the immediate task; no awareness of scale or failure.
2
Needs Improvement
Limited architectural awareness; struggles with design decisions.
3
Satisfactory
Works within an existing design; makes sound local decisions.
4
Very Good
Designs for scale and maintainability; articulates trade-offs clearly.
5
Excellent
Weighs data cost, coverage, and latency openly; explains when microsimulation was overkill and a split failure metric answered the question faster.
03
Evaluation factor
Evidence and rigour
25% weight
Test analytic rigour on signal retiming or safety projects: control corridors, seasonal and weather adjustment, EB crash prediction, confidence in claimed delay or arrival-on-green improvements.
Evidence to listen for
Validates results rather than trusting output
Knows how their work is measured and what a bad result looks like
Can describe a time their own analysis or model was wrong and how they caught it
Careful about data quality, leakage, and silent failure
Five-point scoring guide
1
Poor
Ships unvalidated work; no notion of how correctness is checked.
2
Needs Improvement
Validates superficially; misses obvious quality or leakage issues.
3
Satisfactory
Reasonable checks in place; rigour drops under time pressure.
4
Very Good
Validates thoroughly; can name a real error they caught in their own work.
5
Excellent
Quantifies impacts with baselines and uncertainty ranges, cites Highway Safety Manual or HCM methods, and flags where results were inconclusive.
04
Evaluation factor
Collaboration and communication
15% weight
Assess how they brief traffic engineers, transit operations, city councils, and TMC staff: dashboards in Power BI or Tableau, memos, public Vision Zero or congestion reporting.
Evidence to listen for
Explains technical work to non-technical stakeholders
Gives and takes code or peer review constructively
Documents enough that the work survives their absence
Aligns with team process rather than working around it
Five-point scoring guide
1
Poor
Cannot work in a team; resistant to feedback.
2
Needs Improvement
Communication issues create rework; lone-wolf tendencies.
3
Satisfactory
Adequate team member; documentation and review participation are light.
4
Very Good
Communicates well; reliable reviewer and collaborator.
5
Excellent
Describes a dashboard or memo that actually changed a timing plan or funding decision, translating detector-level detail into plain corridor outcomes.
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.
