Pharmaceutical Data Scientist

This role will collaborate closely with experimental and computational scientists to develop scientific and automated workflows, data-driven approaches, AI/ML models, scientific applications, and informatics solutions that improve how scientific data are accessed, parsed, analyzed, connected, visualized, and used for decision making.

Key Responsibilities

• Develop and apply data science, AI/ML, and informatics approaches to address Pharmaceutical Materials Science challenges.
• Build scientific data workflows, analytics, and visualization tools that enable effective use of experimental and computational data.
• Develop tools and pipelines to extract, transform, standardize, and integrate data from experimental & computational workflows.
• Automate repetitive or manual scientific data-processing and analysis workflows to improve efficiency, reproducibility, and scalability.
• Design, develop, validate, and deploy computational tools and scientific applications supporting research and development workflows.
• Integrate diverse scientific datasets to enable data-driven insights, predictive modeling, and decision making.
• Partner with experimental and computational scientists to identify high-value opportunities and translate scientific needs into practical digital solutions.
• Promote reproducible scientific computing, good software-development practices, and adoption of data and AI capabilities across the organization.

Qualifications

• Bachelors degree in Data Science, Computer Science, Artificial Intelligence/Machine Learning, Bioinformatics, Cheminformatics, Computational Science, Chemistry, Materials Science, Chemical Engineering, or a related quantitative/scientific discipline.
• Strong programming skills, preferably in Python, with demonstrated ability to analyze data and develop computational solutions.
• Experience with Pharmaceutical data analysis, processing, scientific computing, statistics, machine learning, AI, or scientific informatics.
• Experience working with chemistry, pharmaceutical, materials-science, or laboratory-generated scientific data.
• Demonstrated ability to work with complex and heterogenous scientific datasets, including extracting, transforming, integrating, and analyzing structured and/or unstructured data.
• Translate scientific questions into data-driven analyses or computational solutions.
• Familiarity with modern data-science, scientific computing, and machine-learning tools, libraries and frameworks.
• Strong analytical and problem-solving skills.
• Effective written and verbal communication skills and the ability to collaborate within multidisciplinary scientific teams.

Preferred Qualifications

• Experience developing scientific software, analytical applications, visualization tools, dashboards, automated workflows, or APIs.
• Experience with scientific databases, data integration, metadata, ontologies, knowledge graphs, or scientific informatics.
• Hands-on experience developing or applying machine-learning, deep-learning, generative-AI, or large-language-model approaches.
• Familiarity with modern software-development practices, including Git/version control, testing, documentation, and reproducible computing.
• Experience developing interactive scientific applications and user interfaces, including notebook-based environments such as Jupyter/JupyterLab and web-based frameworks or visualization platforms.
• Experience with cloud computing, high-performance computing, or scalable data-processing environments.
• Demonstrated ability to take a scientific problem from raw data to structured data to analysis/model to automated workflow or usable computational solution.