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 build depth: LoRaWAN or NB-IoT sensor nodes, ISOBUS or CAN bus tractor data, NDVI processing from Sentinel-2 or drone imagery, Python, PostGIS, time-series stores.
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 stacks and field hardware, explains soil moisture calibration or imagery pipelines with detail only a builder would recall.
02
Evaluation factor
Systems and trade-offs
25% weight
Probe architecture calls for rural conditions: intermittent connectivity, edge versus cloud inference, battery budgets on field nodes, data volumes from daily satellite passes versus hourly telemetry.
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 offline buffering, gateway placement, and power draw against cost per hectare, admitting what they sacrificed and why.
03
Evaluation factor
Evidence and rigour
25% weight
Assess how they validated agronomic claims: yield model backtests, ground-truth soil sampling against sensor readings, irrigation trial plots, false alerts on pest or disease detection models.
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
Cites measured outcomes (water saved, yield lift, alert precision) with control plots or baselines, not vendor brochure numbers.
04
Evaluation factor
Collaboration and communication
15% weight
Look for work with agronomists, farm managers, and co-op staff: translating scouting practice into features, training operators, handling data ownership concerns with growers.
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 field visits and iterations driven by grower feedback, showing respect for agronomic knowledge over pure software assumptions.
Put this rubric to work
Score every candidate against the same standard
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