Data Science Engineer (Chemistry)

We are looking for an experienced freelancer with expertise in cheminformatics, data science, and R&D digitalization to support the implementation and enhancement of an enterprise R&D data management platform used in chemical research and formulation development. The consultant will work closely with our digitalization and scientific teams to improve data quality, develop scientific workflows, and evaluate advanced analytics capabilities. Key Responsibilities 1. R&D Data Integration & Transformation Design and implement data cleaning, validation, and transformation workflows using REST APIs and Python. Integrate experimental, master, and transactional data into the R&D data management platform. Develop reusable ETL pipelines and automation scripts to improve data consistency and reliability. 2. Cheminformatics Workflow Development Develop cheminformatics workflows to calculate, enrich, and manage molecular and ingredient properties. Generate molecular descriptors, fingerprints, and other chemical representations using industry-standard libraries. Integrate computed properties into the R&D data management solution for downstream analytics and machine learning. 3. Machine Learning Capability Assessment Evaluate the built-in machine learning capabilities of the R&D data management platform. Execute representative business use cases in formulation and synthesis R&D. Identify functional gaps, technical limitations, and enhancement opportunities. Translate findings into a prioritized product backlog with clear business value. 4. Scientific Data Visualization Assessment Assess existing visualization and reporting capabilities within the platform. Develop representative dashboards and scientific visualizations for R&D users. Identify usability improvements, missing analytical capabilities, and reporting requirements. Prioritize enhancement requests based on business impact. 5. Data Quality Monitoring & Governance Design automated data quality monitoring solutions for master data and ELN transactional data. Define and implement quality rules covering completeness, consistency, validity, uniqueness, and integrity. Develop dashboards and KPIs to continuously monitor data quality across R&D processes. Recommend governance improvements to increase data reliability and usability.