MODULE LEAD - LEAD

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 2–3 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.