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Company Description:
DigAIT is a scientific technology and consulting partner that helps organizations in the chemical and life sciences industries accelerate digital transformation. The team integrates AI, IT, scientific data, and domain expertise to modernize processes across research, development, laboratories, manufacturing, and operations. DigAIT’s capabilities span process modeling, digital process transformation, laboratory and manufacturing solutions, data analytics, business intelligence, cheminformatics, bioinformatics, and advanced AI, including LLMs and generative models. The company supports clients from process assessment and future-state design through implementation, data integration, analytics, and AI adoption, leveraging platforms such as SAP Signavio, LIMS, ELN, MES, ERP, Power BI, Tableau, AWS, Azure, Python, and modern AI/LLM tools. DigAIT’s mission is to make scientific processes more connected, intelligent, data-driven, and scalable for its global partners.
Responsibilities
- Review, curate, validate, and consolidate Chemistry R&D master data from multiple sources.
- Perform detailed data profiling and quality assessment to identify inconsistencies, duplicates, missing information, anomalies, and data-integrity risks.
- Harmonize chemical and scientific data according to established data-governance, quality, and standardization rules.
- Identify and flag cases that fall outside existing rules or require expert scientific judgement.
- Assess equivalence and consistency of scientific data, including chemical names, descriptions, properties, units, and other metadata.
- Apply scientific unit conversions, normalization, and equivalence assessment to support master-data harmonization.
- Use text clustering, pattern analysis, and other data-analysis techniques to identify relationships, duplicates, and potential data inconsistencies.
- Work independently through data-review and consolidation activities, documenting decisions, assumptions, and exceptions clearly.
- Support the definition and refinement of data-quality and harmonization rules based on findings from the curation process.
- Use scripting and data-analysis tools, where appropriate, to automate repetitive curation and analysis tasks.
- Leverage LLMs and modern agentic/AI platforms to accelerate data review, classification, pattern identification, and harmonization activities.
- Validate AI-assisted outputs and ensure that final decisions meet scientific accuracy and data-governance requirements.
- Collaborate with Chemistry R&D, data, IT, and digitalization teams to resolve complex data issues and improve overall data quality.
- Contribute to the development of repeatable processes and best practices for scientific master-data management.
Qualifications
- MSc or PhD in Chemistry, Chemical Engineering, Materials Science, or a related scientific discipline.
- Practical experience in laboratory environments, Chemistry R&D, or scientific data management is strongly preferred.
- Experience in data curation, data governance, master-data management, data harmonization, or data-quality management.
- Strong understanding of scientific and chemical data and the ability to interpret chemical terminology, properties, and metadata.
- Proficiency in data profiling, pattern analysis, and text clustering.
- Familiarity with scientific unit conversions, normalization, and equivalence assessment.
- Comfortable working with semi-structured, heterogeneous, and large datasets.
- Basic scripting/programming skills, preferably Python, for data analysis and automation.
- Familiarity with ELN, LIMS, R&D data-management platforms, or similar scientific information systems is a distinct advantage.
- Comfortable using LLMs, AI-assisted tools, and agentic platforms as part of modern data-curation and analysis workflows.
- Ability to critically assess and validate AI-generated outputs rather than relying on them without verification.
- Excellent attention to detail, with a strong focus on data accuracy and consistency.
- Strong structured and analytical thinking, problem-solving skills, and independent judgement.
- Ability to work independently and make evidence-based decisions when data falls outside predefined rules.
- Strong written communication skills, particularly the ability to clearly document findings, decisions, exceptions, and recommendations.
- Ability to work effectively with multidisciplinary teams across scientific, data, IT, and digital R&D functions.
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