Small Molecule Data Analyst

Key Responsibilities

1. Pharmaceutical Data Analysis & Management: Familiar with the R&D process for chemical drugs. Able to accurately translate pharmaceutical clients' business needs into data product requirements.

2. Cross-functional Collaboration & Data Governance: Work closely with data engineering, data quality, and AI teams to ensure the efficient and stable operation of data mining and processing pipelines, guaranteeing the accuracy and usability of output data.

3. Data Quality System Development: Build and continuously refine the biomedical data quality evaluation system. Leverage customer feedback loops, data-driven analytical methods, and business rule optimization to consistently improve overall data quality.

4. Project Coordination & Management: Act as the primary interface for data operations. Coordinate cross-functional data projects and effectively manage project timelines and deliverables.

5. AI-Driven Data Processing: Proficient in Prompt Engineering with the ability to design and optimize prompts. Lead the application of Large Language Models (LLMs) to perform intelligent information extraction, normalization, and knowledge mining from unstructured content (e.g., patents, academic literature). Explore and build data processing Agents tailored for the biomedical field, such as drug R&D and patent strategy (patent layout).

Qualifications & Requirements

1. Educational Background: Master’s degree or above in Synthetic Chemistry, Organic Chemistry, Computational Chemistry, Drug Design, AIDD (AI in Drug Discovery), or other chemistry-related fields.

2. Industry Experience: Candidates with data-related work experience in chemical drug R&D, clinical research, pharmaceutical analysis, or database construction at large domestic or global pharmaceutical companies are preferred.

3. Professional Skills: Proficiency in using biomedical/chemical databases such as SciFinder, Reaxys, STN, or GOSTAR is highly preferred.

4. Technical Skills: Ability to perform basic data processing using SQL, Python, etc. Experience in database design and practical implementation is a plus.

5. AI Technologies: Proficient in mainstream Large Language Models (LLMs) such as DeepSeek, GPT, Claude, and Gemini. Rich experience in AI applications and algorithms is preferred.

6. Comprehensive Abilities: Excellent logical thinking, business acumen, and fast-learning capabilities. Able to accurately translate complex business needs into data solutions. Outstanding cross-departmental communication, coordination, and project execution skills.

7. Language Skills: English as a working language (proficient in listening, speaking, reading, and writing). Excellent cross-cultural communication skills.