Current life science benchmarks fail to capture the messy, iterative reality of real research work. They tend to ask clean fact-recall or prediction questions with neat reference answers, but practicing scientists spend their time interpreting incomplete evidence, reconciling conflicting results, designing difficult experiments, and making decisions under uncertainty. **LifeSciBench** was built to close this gap by measuring whether AI systems can actually support the workflows that matter in applied biotech and pharmaceutical research, not just answer biology trivia. It was designed by 173 PhD-level scientists with direct drug-discovery experience, and every task reflects a realistic request a scientist might make to a knowledgeable collaborator.