job description
Join Arcanys, a cutting-edge technology company at the intersection of healthcare and artificial intelligence, as an Applied Machine Learning Scientist specializing in Digital Biomarkers. In this pivotal role, you will develop and deploy advanced statistical and machine learning models that transform behavioral app data into clinically validated digital biomarkersâbridging the gap between raw data and actionable health insights.
As part of our Medical Software Intelligence (MSI) team, youâll work on high-impact projects that leverage mobile health data, wearables, and real-world evidence to enable early disease detection, personalized treatment, and remote patient monitoring. Your models will directly contribute to improving patient outcomes, reducing healthcare costs, and advancing precision medicine.
This is a fully remote opportunity based in Bali, Indonesia (Canggu, Ubud, Denpasar, Jimbaran, Nusa Dua, Kuta, or Badung), offering a flexible work environment with a global team of experts. Youâll collaborate with data scientists, software engineers, and healthcare professionals to build scalable, production-ready ML systems that meet regulatory and clinical standards.
If youâre passionate about applying AI to healthcare and want to work on projects that make a real difference, weâd love to hear from you. Join us in shaping the future of digital health!
Responsibility
- Design, develop, and validate machine learning and statistical models to extract digital biomarkers from behavioral app data, wearables, and other digital health sources.
- Collaborate with cross-functional teams (data engineers, software developers, and clinicians) to integrate ML models into production systems while ensuring scalability, reliability, and compliance with healthcare regulations.
- Apply feature engineering, time-series analysis, and deep learning techniques to improve model accuracy and interpretability for clinical applications.
- Conduct exploratory data analysis (EDA) and A/B testing to identify patterns, validate hypotheses, and optimize model performance.
- Develop automated pipelines for data preprocessing, model training, and deployment using tools like TensorFlow, PyTorch, or scikit-learn.
- Work closely with healthcare domain experts to ensure models align with clinical needs and regulatory requirements (e.g., HIPAA, GDPR, or local health data laws).
- Publish research findings, contribute to whitepapers or patents, and present results to stakeholders and external partners.
- Stay updated on emerging trends in AI, digital health, and biomarker research to drive innovation within the team.
Qualifications
- Masterâs or PhD in Computer Science, Machine Learning, Statistics, Biomedical Engineering, or a related quantitative field.
- 3+ years of hands-on experience in applied machine learning, with a focus on healthcare, biometrics, or digital biomarkers (industry or research).
- Proficiency in Python and ML frameworks (TensorFlow, PyTorch, scikit-learn) for model development and deployment.
- Experience with time-series data, NLP, or computer vision (depending on project needs) and tools like Pandas, NumPy, and Jupyter Notebooks.
- Strong background in statistical modeling, experimental design, and hypothesis testing for clinical or real-world data.
- Familiarity with healthcare data standards (e.g., FHIR, HL7) and regulatory compliance (e.g., HIPAA, GDPR) is a plus.
- Experience with MLOps tools (e.g., MLflow, Kubeflow, Docker) and cloud platforms (AWS, GCP, or Azure) for scalable model deployment.
- Excellent problem-solving skills, with the ability to translate complex technical concepts for non-technical stakeholders.
- Strong communication skills in English (written and verbal) for collaboration with global teams.