Roles & Responsibilities:
- Lead end-to-end research initiatives in medical/biomedical domains, including hypothesis formulation, study design, and interpretation.
- Apply advanced mathematics, biostatistics, modelling, and statistical inference to analyse medical or biomedical datasets.
- Develop and implement Python-based data pipelines, exploratory analysis, and model development.
- Build, evaluate, and optimize Machine Learning and Deep Learning models for research insights, biomarker identification, and healthcare signal analysis.
- Conduct systematic literature reviews, scientific evidence synthesis, and meta-analysis to support scientific positioning and product decisions.
- Translate research outcomes into digital/IT deliverables, collaborating closely with data scientists, clinicians, and engineering teams.
- Prepare and support scientific publications, whitepapers, and research documentation.
- Review, analyse, and summarize biomarker literature, clinical frameworks, and emerging healthcare trends.
- Contribute to regulatory/clinical validation activities if required and mentor junior researchers or analysts.
Required Skills & Qualifications:
- PhD in healthcare-related discipline (Life Sciences, Biomedical Engineering, Molecular Biology, Physiology, Clinical Research, etc.).
- Minimum 23 years of IT/industry experience applying scientific or analytical skills in digital health, AI/ML, or research environments.
- Combined academic and professional experience totalling 8+ years (Professor/Research Fellow experience included).
- Very strong mathematics and statistical fundamentals, including research methodology and inference.
- Hands-on experience in Python (NumPy, Pandas, SciPy, Scikit-learn, PyTorch/TensorFlow).
- Proven exposure to Machine Learning, Deep Learning, model development, validation, and evaluation frameworks.
Preferred / Added Advantages
- Background in life sciences or biomedical sciences.
- Prior experience with biomarkers, translational medicine, precision medicine, or aging research.
- Experience handling RWE, high-dimensional biomedical data, omics datasets, or clinical datasets.
- Experience writing or contributing to peer-reviewed publications.
