Applied ML Scientist, Clinical Risk Modelling

About the role

Build and validate clinical-prediction models on de-identified EHR data for a GLP-1 companion project. Arcanys is hiring a Cebu-based, fully remote Applied ML Scientist to work inside a U.S. partner’s secure environment with an obesity-medicine advisor.

What you'll do

• Build retrospective patient cohorts from longitudinal EHR data.
• Engineer time-windowed features from medical records, structured to avoid temporal leakage.
• Develop and internally validate models for GLP-1 discontinuation and post-discontinuation weight regain.
• Apply clinical-prediction statistics: discrimination, calibration, and subgroup performance.
• Translate model outputs into interpretable risk tiers with clinical advisors.
• Document datasets, modelling decisions, and validation results for reproducibility and future production.

Requirements

• Background in statistics, biostatistics, epidemiology, health data science, machine learning, or a related quantitative discipline.
• Around 3-6+ years developing clinical or tabular predictive models, with strong applied statistics.
• Fluent in tabular ML (gradient boosting, regularized regression) and clinical-prediction methodology, especially calibration and validation.
• Experience engineering features from EHR or other longitudinal records, with a sharp eye for data leakage.
• Fluent in Python and the scientific Python ecosystem, including Pandas, NumPy, scikit-learn, and related libraries.
• Comfortable in restricted clinical data environments and with health-data privacy (HIPAA, GDPR).

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