Puspendu Sardar
Associate @Cheeky Scientist
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WORK HISTORY
Associate @Cheeky Scientist
EDUCATION
Kiel University
Doctor of Philosophy - PhD, Molecular Genetics
International Max-Planck Research School for Evolutionary Biology
Doctor of Philosophy - PhD, Molecular Genetics
National Institute of Science Education and Research
Master of Science - MS, Biology/Biological Sciences, General
ABOUT PUSPENDU SARDAR
I am a senior bioinformatician and computational biologist specialising in microbiome, metagenomics, and multi-omics data analysis, with a strong track record of translating complex NGS data into actionable biological and translational insights.My work sits at the intersection of large-scale sequencing data, machine learning, and experimental microbiology. I design and deliver end-to-end, reproducible analysis pipelines for metagenomics, metatranscriptomics, and integrative multi-omics studies, from raw data to interpretation and decision-ready outputs.Over 12+ years, I’ve contributed to projects spanning disease biomarker discovery (IBD, cancer), host-microbiome interactions, antibiotic resistance and resistome profiling, and environmental microbiome analysis. I routinely work with high-dimensional datasets, applying statistical modelling and ML approaches to uncover meaningful biological signals at scale.What differentiates me is my ability to bridge computation and wet-lab biology, from anaerobic culture of non-model microbes and functional gene annotation to metabolic pathway reconstruction and multi-omics integration. This allows me to collaborate effectively across computational, experimental, and translational teams.I’m now focused on senior-level roles in industry (biotech, pharma, diagnostics, microbiome-focused companies) where advanced data analysis directly informs products, pipelines, and biological decision-making. Open to conversations around Senior Bioinformatician, Computational Biologist, or Scientist roles in microbiome and multi-omics research.Keywords: microbiome, metagenomics, bioinformatics, NGS, multi-omics, machine learning, biomarker discovery, MAGs
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