Formulation Domain Specialist (Knowledge Architect)

About the role

Patsnap’s Materials team is building structured formulation knowledge from patents, scientific literature, and technical sources to support its Formulation Agent. The role involves defining how formulation knowledge—ingredients, quantities, processes, properties, and claims—is represented, evaluated, and linked to underlying evidence, working closely with formulation scientists, data scientists, engineers, and product managers.

Key responsibilities

• Define and maintain the knowledge model for formulations, covering ingredients, functions, concentrations, processes, properties, test methods, and performance results.
• Establish evidence and quality standards, specifying how proposed, prepared, and tested formulations are distinguished and how provenance, confidence, and conflicting information are represented.
• Develop normalization rules that connect formulation information across patents, scientific literature, datasheets, and other technical sources.
• Build guidelines, benchmarks, and evaluation datasets for formulation extraction, search, and AI‑generated outputs.
• Collaborate with product, data science, and engineering teams to translate expert judgment and user feedback into data and product improvements.
• Lead review and resolution of complex formulation‑data questions and support quality management of internal or external curation work.

Required profile

• Degree in formulation science, chemistry, polymer science, materials science, chemical engineering, pharmaceutical science, cosmetic science, or a related field.
• At least 5 years of relevant experience in formulation R&D, scientific information analysis, technical data curation, or knowledge management.
• Strong understanding of formulation design, including ingredient functions, concentrations, processing conditions, test methods, and performance relationships.
• Ability to evaluate evidence across patents, scientific papers, technical datasheets, and experimental records.
• Experience developing structured domain knowledge through schemas, taxonomies, controlled vocabularies, normalization rules, or similar approaches.
• Ability to translate scientific judgment into clear, repeatable guidelines and quality standards.
• Strong project leadership and cross‑functional communication skills.
• Professional working proficiency in English and Chinese.

Required skills

• Knowledge graphs
• Entity resolution
• Chemical structure data
• Information retrieval
• Search evaluation
• LLM‑based product retrieval and evaluation
• Patent analysis, prior‑art search, novelty assessment, freedom‑to‑operate analysis
• Schemas, taxonomies, controlled vocabularies, normalization rules
• Experimental data management, design of experiments, ELN, LIMS