Founding Data Scientist

Founding Data Scientist Mira, Serbia

Mira is building the future of women's hormonal health through quantitative hormone tracking technology.

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.

Requirements:

Strong Python and data science skills; SQL preferred and experience with longitudinal, time-series, behavioral, health, wearable, physiological, or biomarker data;

Experience using model interpretability and explainability methods: SHAP, LIME, etc;

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;

Experience moving ML or algorithmic features toward production.

Working conditions:

Remote position;

40-hour workweek;

Flexible working hours;

Occasional overlap with the US team;

Competitive salary with performance-based bonus system tied to OKRs;

Fully remote environment that supports autonomy, ownership, and independence;

Real opportunities for career growth, with the ability to shape, build, and develop teams as we scale.

Apply

#serbia

What You'll Do

• 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;

Basic Qualifications

• Strong Python and data science skills; SQL preferred and experience with longitudinal, time-series, behavioral, health, wearable, physiological, or biomarker data;
• Experience using model interpretability and explainability methods: SHAP, LIME, etc;
• 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;