Life Science Scientist

About the role

PatSnap is building the next generation of AI-native products for life sciences, moving beyond a traditional SaaS platform into an intelligent ecosystem powered by MCPs, Skills and Agents. It is looking for a domain knowledge scientist who combines deep life sciences expertise with product judgment: turning complex, real-world life sciences problems into focused, usable products. The role is in-office in the Singapore office.

Key responsibilities

• Identify high-value life sciences workflows (clinical development, translational medicine, business development, competitive intelligence, regulatory affairs) where AI can improve how work gets done.
• Spend meaningful time with researchers, scientists, BD teams and clinical operations professionals to understand their daily work and the last steps where manual research or spreadsheets are used.
• Turn PatSnap's life sciences data (clinical trials, sequences, targets, deals, patents) into AI-native products using MCPs, Skills and Agents to solve specific workflow problems.
• Prototype and launch quickly, getting early capabilities into users' hands in research-preview mode within weeks, then improve them from customer feedback and observed behavior.
• Design for different scientific and commercial users and make strong prioritization decisions on what to build.
• Act as a domain authority for life sciences products, helping product and engineering teams understand the scientific and industry context.
• Partner with marketing and go-to-market teams so positioning and content reflect the genuine value of the products.

Required profile

• PhD in life sciences, bioinformatics, computational biology or a related field, or equivalent deep industry experience.
• Strong knowledge of pharma, biotech, clinical development, translational medicine, BD, competitive intelligence or another relevant life sciences area.
• Understanding of how scientists and life sciences professionals actually work, including the manual parts not captured in formal processes.
• Curiosity about how AI and agents will reshape life sciences workflows, and comfort working with uncertainty and building at the edge of current AI capabilities.
• Ability to communicate complex scientific concepts clearly across technical, product and commercial teams.
• Experience delivering something end to end, such as a research paper, scientific tool, internal product, data pipeline or workflow solution.

Required skills

• Even better: hands-on experience with LLMs, AI Agents, MCP or prompt engineering.
• Experience with structured biomedical data such as clinical trials, sequences, target/disease ontologies, deals or patents.
• Experience building products or tools for researchers, data scientists or scientific analysts.
• Open-source contributions, technical writing or a portfolio demonstrating the ability to build.