Why pre-screen femtech developers before the technical panel
Cycle, fertility and pregnancy data carries risks that ordinary product data does not, including legal exposure that varies by jurisdiction and changes. A prediction presented too confidently can also be read as contraception advice by someone relying on it. Developers worth hiring have thought about both. A short screen asks what data they decided not to collect and why.
What actually matters when screening Femtech Developer candidates
- 01
Technical proficiency
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.
- 02
Systems and trade-offs
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.
- 03
Evidence and rigour
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.
- 04
Collaboration and communication
Look for work alongside clinicians, midwives or reproductive endocrinologists, plus handling of GDPR special category data with legal, and user research with menstruating testers.
Pre-screening questions to ask Femtech Developer candidates
12 questions grouped by what they test. Ask the same set in every screen and score answers on a consistent scale, or send them as an async video screen and compare answers side by side.
Shipped in this space
3 questions01Which product in this area are you most proud of working on?
Listen forA shipped product with users, and the part they built described rather than the company story.
Prototypes presented as products, or contribution described only at team level.
02Have you worked on a mobile application for women's health?
Listen forReleased applications with the platform work described, including data sync and offline handling.
Health apps described generically, or no release ever reaching an app store.
03Do you have experience with products covering cycles, fertility or menopause?
Listen forDomain understood well enough to know where predictions are weak and users are underserved.
Domain treated as a data problem, or the physiology never actually learned.
Engineering is solid
4 questions04Which languages and technologies have you used on these products?
Listen forA stack they know deeply, with choices explained by the product rather than by preference.
Technologies listed without depth, or choices driven by what was newest.
05Do you have experience building APIs for health technology?
Listen forInterfaces designed with authentication, audit and versioning treated as requirements from the start.
Health APIs built like ordinary endpoints, or no audit trail on sensitive records.
06What is your experience with large datasets and predictive models here?
Listen forModel limits understood, with uncertainty surfaced in the interface rather than hidden by a number.
Predictions presented as certainty, or accuracy claimed without validation on real users.
07Can you describe a complex problem you solved in this space?
Listen forA specific technical problem with the reasoning described, including approaches that failed.
Problems described as generic engineering, or no detail beyond the outcome.
Data minimised
3 questions08How familiar are you with the data protection rules that apply to this data?
Listen forHealth data treated as a special category, with jurisdictional differences and retention understood.
Regulation described loosely, or health data handled like ordinary personal data.
09How do you think about privacy and security in these products?
Listen forMinimisation as the default, with a specific field they chose not to collect and why.
Everything collected for future analysis, or privacy handled entirely in the policy document.
10What challenges have you faced building in this area, and how did you handle them?
Listen forReal difficulties named, including sensitivity, trust and the limits of the underlying data.
Challenges described as funding or market education, or nothing about the data itself.
Clinicians involved
2 questions11Have you worked with clinicians, researchers or designers on these products?
Listen forClinical input sought before health-related claims shipped, with advice acted on rather than noted.
Health claims made without clinical review, or clinician feedback treated as optional.
12How would you make a product in this area accessible to a wide range of users?
Listen forAccessibility, reading level and inclusive language considered, with users outside the core case included.
One user profile assumed, or accessibility deferred until after launch.
How to score responses
Score every candidate on the same four criteria immediately after the screen. At this stage you are shortlisting for panel interviews, not making the final call.
Technical proficiency
35%5Names the stack and health data APIs used, describes prediction or symptom-logging features shipped, and explains their own code contribution precisely.
Systems and trade-offs
25%5Weighs privacy, latency and model accuracy openly, citing a real architecture decision on storing or processing reproductive health records.
Evidence and rigour
25%5Quotes concrete accuracy metrics and describes how irregular cycles or perimenopause users were tested rather than assumed.
Collaboration and communication
15%5Describes translating clinical guidance into product logic and pushing back on a feature for privacy or medical accuracy reasons.
This data carries legal exposure ordinary product data does not. A one-way video screen asks what they left out.
Try it on HirevireScreening FAQ
Process basics
How long should a pre-screening round for this role take?
Fifteen minutes across eight to ten questions, answered async. Enough to establish products they shipped, test their engineering, and hear how they handle sensitive health data.
Do they need a clinical background?
No, but they need to know where their competence stops and to work with clinicians on anything that touches a health claim. Developers who make those calls alone create real risk.
Evaluating answers
What is the strongest signal when screening this role?
Data they chose not to collect. Developers who take this seriously can name a field they left out and explain the reasoning. Anyone collecting everything has not considered the exposure.
How do I judge their handling of predictions?
Ask how uncertainty is presented in the interface. Careful answers show ranges and avoid implying reliability the model does not have, especially where users may act on it.
Go deeper on this role
Sanat has been hiring since 2012 and watching the recruitment industry change up close ever since, and turned that screening process into Hirevire's video screening platform. LinkedIn
