Maths for Biotech Jobs: The Only Topics You Actually Need (& How to Learn Them)
Biotechnology is packed with data. Whether you are applying for roles in drug discovery, clinical research, bioprocessing, diagnostics, genomics or regulated manufacturing, you will meet numbers every day: assay readouts, QC trends, dose response curves, sequencing counts, clinical endpoints, stability profiles, validation reports & risk assessments. If you are a UK job seeker moving into biotech from another sector or you are a student in biology, biochemistry, biomedical science, pharmacy, chemistry, engineering or computer science, it is normal to worry you “do not have the maths”. What biotech roles do need is confidence with a small set of practical topics that show up again & again. This guide focuses on the only maths most biotech job adverts quietly assume: • Biostatistics basics for experiments, evidence & decision making • Probability for variability, uncertainty & risk • Linear algebra essentials for omics, PCA & modelling workflows • Calculus basics for kinetics, rates & dose response intuition • Simple optimisation for curve fitting, process set points & model tuning
