Small Molecule Data Analyst

Socket.dev; United States; On-site; USD 110,000 - 170,000; Full time

Job summary

Socket.dev in the United States is seeking a data-focused professional to drive pharmaceutical data analysis and management. You will translate client R&D needs into concrete data product requirements and work across engineering, QA, and AI teams to ensure robust pipelines.

The role involves developing data quality evaluation systems, coordinating cross-functional data projects, and applying AI techniques like prompt engineering and LLMs to extract and organize knowledge from scientific content.

Qualifications

• Master’s degree or higher in chemistry-related fields such as synthetic/organic/computational chemistry.
• Experience with data-related work in chemical drug R&D, clinical research, or pharmaceutical analysis preferred.
• Proficiency with biomedical/chemical databases like SciFinder, Reaxys, STN, or GOSTAR.
• Ability to perform basic data processing with SQL, Python; database design is a plus.
• Familiarity with AI models and prompt engineering is valued.
• Excellent communication and cross-department collaboration skills.
• English working language with strong written/verbal skills.

Responsibilities

• Pharmaceutical data analysis and management: translate clients' drug R&D needs into data product requirements.
• Collaborate with data engineering, quality, and AI teams to ensure robust pipelines and usable outputs.
• Develop and refine biomedical data quality evaluation systems using feedback and data-driven methods.
• Coordinate cross-functional data projects and manage timelines and deliverables as data operations lead.
• Apply AI-driven data processing, including prompt engineering and LLMs, for information extraction and knowledge mining.

Skills

English proficiency

Strong analytical thinking

Education

Master’s degree or above in chemistry-related fields

Tools

SciFinder

Reaxys

STN

GOSTAR

SQL

Python

Job description

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.