Gp Gajinder Singh
Lead Scientist @nference
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WORK HISTORY
Lead Scientist @nference
Bengaluru, IN
Lead the design and execution of analytical studies on multi-million–record EHR datasets (structured fields, clinical notes, cardiac imaging, and molecular data) to support drug development, safety surveillance, and outcomes research for life sciences clients.Designed and operationalized machine learning systems for ECG-based cardiovascular health monitoring, integrating signal processing, model validation, and clinical interpretability.
EDUCATION
Delhi University
Bachelor of Science (B.Sc.), Microbiology
Institute of genomics and integrative biology (CSIR)
Doctor of Philosophy - PhD, Biomathematics, Bioinformatics, and Computational Biology
Madurai Kamaraj University
Master of Science (M.Sc.), Biotechnology
ABOUT GP GAJINDER SINGH
I am a PhD-trained bioinformatics scientist with over 8 years of experience in Real-World Data (RWD) and Real-World Evidence (RWE), working at the intersection of computational science, biology, and clinical research.My work focuses on translating complex, high-dimensional healthcare data into rigorous, decision-grade evidence for life sciences organizations. I design and deploy machine learning, NLP, and AI-driven solutions that are scientifically sound, methodologically transparent, and aligned with real-world clinical needs.With a background spanning academia and industry—including first- and corresponding-author publications—I bring both scientific depth and practical execution to healthcare data science. I have led cross-functional teams and collaborated closely with global stakeholders to support drug development, observational research, and patient outcomes initiatives.I am particularly interested in building systems that are not only technically advanced, but also reproducible, interpretable, and ethically grounded.Core Expertise• Real-World Data (RWD) and Real-World Evidence (RWE) strategy and analytics• Advanced machine learning, deep learning, NLP, and LLM applications in healthcare• EHR/EMR data modeling, strengths, limitations, and bias considerations• Genomics and next-generation sequencing (NGS) analytics• Medical terminologies and ontologies (ICD, CPT, RxNorm, SNOMED)• Validation frameworks, benchmarking, and quality assurance of ML systems• Translating scientific insight into commercially and clinically meaningful solutions• Cross-functional leadership and stakeholder engagement
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