Founding Data Scientist

Mira is building the future of women's hormonal health through quantitative hormone tracking technology. We're seeking an exceptional Data Scientist to be the founding member of our data science function. This is a unique opportunity to define and build the algorithmic foundation that will help millions of women better understand their reproductive health.

As our first Data Scientist, you will own the entire research agenda - from defining what to measure and why, to building production-ready algorithms that interpret complex hormonal data across diverse populations. You'll work at the intersection of reproductive biology, machine learning, and regulated medical device development, translating cutting-edge science into features that ship in our consumer product.

This role reports to the Head of R&D and will eventually grow into leading a data science team as we scale.

Requirements

Must-have:

• Strong Python and data science skills; SQL preferred.

• Experience with longitudinal, time-series, behavioral, health, wearable, physiological, or biomarker data.

• Experience in consumer health, digital health, wearables, diagnostics, medical devices, SaMD, or IVD.

• Strong model validation judgment, including handling missing data, avoiding leakage, testing robustness, and evaluating performance across user segments.

• Ability to translate data into user-facing product features, metrics, recommendations, or algorithms.

• Experience building models, scores, or analytical frameworks that influenced product, clinical, or business decisions.

• Strong communication skills with both technical and non-technical stakeholders.

• High ownership, startup speed, and comfort working with ambiguity.

• Good scientific judgment; able to distinguish product insights from clinical or marketing claims.

Nice-to-have:

• Experience with menstrual cycle, fertility, reproductive health, hormones, or women’s health data.

• Advanced degree in a quantitative, biomedical, physics, or health-related field.

• Experience with Bayesian statistics, signal processing, causal inference, survival analysis, or longitudinal modeling.

• Experience moving ML or algorithmic features toward production.

• Familiarity with clinical validation, regulatory documentation, claim substantiation, or peer-reviewed research.

Responsibilities

• Build and improve biometrics, algorithms, and data-driven insights for the Mira App.

• Analyze hormone, cycle, app behavior, and user outcome data to identify patterns and product opportunities.

• Develop and validate models for ovulation prediction, cycle modeling, hormone interpretation, and personalized recommendations.

• Partner with Product, Design, Engineering, R&D, and the Real-World Evidence Scientist to translate data into app features and evidence-backed insights.

• Support RWE and clinical research projects with data extraction, exploratory analysis, modeling, and validation.

• Define model-performance and validation frameworks, including uncertainty, subgroup performance, missing data, and edge cases.

• Improve data quality by identifying instrumentation gaps and recommending better data capture in the app.

• Document model logic, assumptions, limitations, and appropriate use cases clearly for technical and non-technical teams.

• Work with Engineering to move prototypes toward production-ready app features or data pipelines.

Benefits

• Join one of the fastest-growing femtech companies in the U.S., driven by a strong mission to advance women’s health and expand access to care

• Be part of a passionate, highly motivated, and international team dedicated to making a real difference in people’s lives

• Thrive in a fast-paced startup environment where your work directly impacts company growth, strategy, and key decisions

• Enjoy real opportunities for career growth, with the ability to shape, build, and develop teams as we scale

• Work in a flexible, fully remote environment that supports autonomy, ownership, and independence

• Receive a competitive salary along with a performance-based bonus system tied to OKRs