Interview scorecard template

Femtech Developer interview scorecard

Evaluate Femtech Developer candidates across 4 weighted areas: technical proficiency, systems and trade-offs, evidence and rigour, and collaboration and communication. Technical proficiency leads at 35%, so check what they built in production: cycle or ovulation prediction logic, HealthKit and Google Fit menstrual data sync, FHIR resources, wearable BBT or HRV ingestion, consent. Use the rubric to compare role-specific evidence consistently.

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TL;DR

For technical proficiency, look for evidence the candidate names the stack and health data APIs used, describes prediction or symptom-logging features shipped, and explains their own code contribution precisely. For systems and trade-offs, look for evidence the candidate weighs privacy, latency and model accuracy openly, citing a real architecture decision on storing or processing reproductive health records. 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.

Evaluation factor

Technical proficiency

35% weight

Check what they built in production: cycle or ovulation prediction logic, HealthKit and Google Fit menstrual data sync, FHIR resources, wearable BBT or HRV ingestion, consent flows.

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

Poor

Cannot work independently; fundamentals are missing.

Needs Improvement

Weak fundamentals; output needs heavy review.

Satisfactory

Competent for the role; needs guidance on complex or unfamiliar work.

Very Good

Strong practitioner; handles hard problems with little guidance.

Excellent

Names the stack and health data APIs used, describes prediction or symptom-logging features shipped, and explains their own code contribution precisely.

Evaluation factor

Systems and trade-offs

25% weight

Probe trade-offs around on-device versus server-side inference for sensitive cycle data, data minimisation, retention windows, encryption at rest, and where they drew the SaMD regulatory line.

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

Poor

No thinking beyond the immediate task; no awareness of scale or failure.

Needs Improvement

Limited architectural awareness; struggles with design decisions.

Satisfactory

Works within an existing design; makes sound local decisions.

Very Good

Designs for scale and maintainability; articulates trade-offs clearly.

Excellent

Weighs privacy, latency and model accuracy openly, citing a real architecture decision on storing or processing reproductive health records.

Evaluation factor

Evidence and rigour

25% weight

Assess how they validated prediction accuracy: cohort backtesting against logged periods, MAE on cycle length, irregular or PCOS edge cases, A/B tests, not just app store ratings.

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

Poor

Ships unvalidated work; no notion of how correctness is checked.

Needs Improvement

Validates superficially; misses obvious quality or leakage issues.

Satisfactory

Reasonable checks in place; rigour drops under time pressure.

Very Good

Validates thoroughly; can name a real error they caught in their own work.

Excellent

Quotes concrete accuracy metrics and describes how irregular cycles or perimenopause users were tested rather than assumed.

Evaluation factor

Collaboration and communication

15% weight

Look for work alongside clinicians, midwives or reproductive endocrinologists, plus handling of GDPR special category data with legal, and user research with menstruating testers.

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

Poor

Cannot work in a team; resistant to feedback.

Needs Improvement

Communication issues create rework; lone-wolf tendencies.

Satisfactory

Adequate team member; documentation and review participation are light.

Very Good

Communicates well; reliable reviewer and collaborator.

Excellent

Describes translating clinical guidance into product logic and pushing back on a feature for privacy or medical accuracy reasons.

Evidence-led prompts

Interview questions for a Femtech Developer

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

01
Which product in this area are you most proud of working on?
02
Have you worked on a mobile application for women's health?
03
Do you have experience with products covering cycles, fertility or menopause?
04
Which languages and technologies have you used on these products?
05
Do you have experience building APIs for health technology?

See the complete Femtech Developer question set

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