Applied ML Scientist, Digital Biomarker

This is a fully remote position, open to applicants in Philippines.

📋 Description

• Create and execute statistical and machine learning models utilizing behavioral and response-time data gathered from in-app tasks.

• Extract features from high-frequency behavioral data and detect significant signals through statistical analysis.

• Evaluate regularized statistical models and gradient-boosted trees, including XGBoost and LightGBM.

• Plan and conduct cross-validation, calibration, explainability, feature-importance, and ablation analyses.

• Develop and sustain reproducible training pipelines, experiment tracking, and model versioning using MLOps methodologies.

• Implement models as scalable APIs and work alongside backend engineers for production integration and low-latency, on-device inference.

• Oversee model performance, data integrity, and model drift while continuously enhancing production models.

• Assist in defining machine learning architecture, engineering standards, and the technical roadmap.

• Collaborate with product, engineering, and clinical teams to convert real-world challenges into effective ML solutions.

• Maintain the modeling pipeline's adaptability to wearables, pupillometry, and EEG modalities.

⛳️ Requirements

• A background in statistics, data science, machine learning, computational neuroscience, biomedical engineering, applied mathematics, or a related quantitative field.

• Approximately 3–6+ years of experience in developing and deploying ML solutions, preferably with behavioral, sensor, or physiological-signal data.

• Strong foundation in applied statistics, feature engineering, supervised learning, model evaluation, and explainability.

• Proficient in Python and the scientific Python ecosystem, including Pandas or Polars, NumPy, and scikit-learn.

• Experience in building production-quality software using Git, Docker, REST APIs (FastAPI or similar), and CI/CD workflows.

• Proven record of models that generalize well, with a disciplined approach to validation and control of overfitting.

• A pragmatic, product-focused mindset aimed at maintainable, production-ready solutions.

• Comfortable handling health data and adhering to privacy regulations under GDPR.

• Experience with digital biomarkers or digital therapeutics is a plus.

• Familiarity with MLOps tools such as MLflow, Dagster, Airflow, or similar is advantageous.

• Experience in deploying models on AWS, OpenStack, Kubernetes, or Docker is beneficial.

• Knowledge in optimizing models for mobile or edge-device inference is a plus.

🏝️ Benefits

• Competitive compensation based on expertise.

• Long-term remote work opportunities.

• Allowances provided.

• Reimbursable allowance up to 15K for home office setup.

• Standard 5-day work week from Monday to Friday.

• Flexible working hours.

• Flat management structure with an open-door policy.

• Opportunity to work with the latest technologies.

• Potential to earn more through the self-training program.

• HMO enrollment for up to 3 immediate dependents starting from day one.

• Incentive-based wellness program.

• Access to Toastmasters, English classes, and various learning opportunities.

• Complimentary consultations with Arcanys registered nutritionist-dietitians.

• Free access to Arcanys CrossFit gym or company-sponsored membership at another gym.