Job Description

Company Description Arcanys is a Swiss-owned software development outsourcing company based in the Philippines, focused on building highly skilled, dedicated teams for tech-enabled organizations of all sizes. The company works as a long-term technology and business partner, aligning closely with client goals rather than operating in a traditional vendor-buyer model. Arcanys complements core software development with mentorship, business analysis, UX/UI design, QA, and 24/7 AWS and customer support. Since 2010, Arcanys has supported more than 200 companies worldwide, including numerous startups, with services spanning web and mobile development, machine learning, AI, and data science. This environment offers applied ML professionals exposure to diverse products, domains, and modern technologies.

Role Description The Applied ML Scientist, Clinical Risk Modelling role is a full-time, remote position focused on designing, implementing, and validating machine learning models to assess and predict clinical risk, with a particular emphasis on oncology and related medical applications. Day-to-day responsibilities include collaborating with clinical and product stakeholders to define modelling objectives, preparing and analyzing clinical datasets, and developing algorithms that translate complex medical and trial data into actionable risk insights. The role involves prototyping and evaluating models, conducting rigorous experiments, and documenting methodologies and results for both technical and non-technical audiences. The Applied ML Scientist will work closely with engineering teams to integrate models into production systems, ensure data quality and compliance with relevant standards, and continuously refine models based on new evidence and user feedback. Participation in research discussions, contribution to clinical trial analytics, and staying current with advances in medical AI and risk prediction are key aspects of the position.

Qualifications

Strong clinical and scientific domain understanding, including Medicine and Oncology, and the ability to interpret clinical outcomes and risk factors.
Experience with clinical Research and Clinical Trials, including study design concepts, endpoints, inclusion/exclusion criteria, and data collection processes.
Demonstrated Laboratory Skills or familiarity with clinical workflows and biomedical data, enabling effective collaboration with healthcare and research teams.
Proficiency in machine learning and statistical modelling (e.g., Python, R, ML frameworks), with experience in risk prediction, survival analysis, or related methods.
Solid skills in data engineering and preparation for clinical datasets, including handling missing data, bias, and class imbalance.
Ability to communicate clearly with multidisciplinary teams, presenting complex findings in accessible terms and writing high-quality technical documentation.
Master's or PhD in Computer Science, Data Science, Biomedical Engineering, Statistics, or a related field; or equivalent practical experience.
Experience working in regulated or health-related environments (e.g., HIPAA, GDPR, clinical data standards) is an