Data specialist / Large Language Models-derived toxicological (meta)data (m/f/d) (LSW)

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ManpowerGroup is the leading global company for staffing solutions.
With our three brands – Manpower, Experis, and Talent Solutions – we assist companies across various industries with their recruitment needs. For 75 years, we've operated in over 75 countries, and throughout Switzerland, we support our clients in successfully completing their tasks and projects.
We are looking for a Data specialist /curator for Large Language Models-derived toxicological (meta)data (m/f/d) (LSW) - Metropolitan Area Basel

Key Responsibilities:

• Data Validation:
Examine toxicological information produced by large language models (LLMs) and cross-check it with primary sources (such as research articles, regulatory documents, and toxicology databases).
Identify and log any discrepancies, errors, or unclear data from the AI-derived output.
Develop a dataset or pipeline (depending on need) to facilitate toxicological assessment tasks.

• Quality Assurance:
Ensure the curated toxicological data is consistent, complete, and scientifically sound.
Follow existing quality control procedures and contribute to optimizing workflows when necessary.

• Toxicology Expertise:
Utilize your toxicological knowledge to review data related to both preclinical and clinical toxicities, mechanisms of toxicity, safety markers, and risk evaluations.
Assess the relevance of curated data and its applicability to specific drug development situations.

• Collaboration:
Collaborate closely with computational toxicologists, data scientists, and other multidisciplinary teams to ensure consistency in curation standards and needs.
Provide constructive feedback to improve the performance of LLMs based on the identification of gaps or errors in the data extracted.

• Documentation and Reporting:
Maintain thorough records of the data validation process and outcomes.
Prepare periodic reports that summarize progress in curation, data quality metrics, and key observations.

Required Qualifications:

• A Master's degree in Biology, Pharmacology, Toxicology, Drug Development, Biotechnology, or a related field (preferred).
• Experience in data curation, annotation, or systematic review approaches is a plus.
• A basic understanding of machine learning, large language models (LLMs), or natural language processing (NLP) tools is desirable, but not essential.
• Proficiency in English (C1 level minimum).
• Exceptional attention to detail and a strong commitment to data accuracy.
• Excellent critical thinking and problem-solving abilities, especially when evaluating scientific data.
• Ability to synthesize complex toxicological data and present clear, actionable insights.
• Strong written and verbal communication skills for reporting and collaboration purposes.