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# Founding Data Scientist

- **Company:** Mira Care
- **Location:** London, United Kingdom (Remote available)
- **Salary:** £93,279
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Biometrics, Data Mining, Python (Programming Language), SQL Databases, SHAP (Shapley Additive Explanations), Model Validation, Data Pipelines
- **Published:** November 13, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5963795953

## About the Role

* 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.

## Description

The role is a remote position, with a 40-hour workweek, flexible working hours, and occasional overlap with the US team.
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.