Master Data Quality Manager

Company Description DigAIT is a scientific technology and consulting partner that helps organizations in the chemical and life sciences industries accelerate digital transformation. The company combines AI, IT, scientific data, and deep domain expertise to improve processes across research, development, laboratories, manufacturing, and operations. Its services span process modeling, laboratory and manufacturing solutions, data analytics, business intelligence, and advanced machine learning and generative AI. DigAIT also provides data stewardship, integration, and knowledge management, supported by strong capabilities in platforms such as SAP Signavio, LIMS, ELN, MES, ERP, Power BI, Tableau, AWS, Azure, Python, and modern AI/LLM tools. The mission is to make scientific processes more connected, intelligent, data-driven, and scalable for clients worldwide.
Role Description
We are looking for a technically strong professional with a background in chemistry, materials science or a related discipline to support our client in profiling the quality, consistency and reliability of formulation, materials and testing data. The role combines scientific domain knowledge with hands-on data quality management. You will work with laboratory teams, formulation scientists, R&D specialists and other business stakeholders to review and profile data, identify inconsistencies, coordinate data cleansing and improve the quality of information used across the organisation.The ideal candidate has practical experience in a laboratory environment, particularly in formulation development, materials testing or product development, and understands how formulation and testing data are generated, recorded, interpreted and maintained.
You should be comfortable working directly with data as well as engaging with subject-matter experts to investigate issues, validate information and ensure that data is accurate, complete, consistent and fit for purpose.
Key responsibilities
1. Data quality monitoring
Monitor and assess the quality of formulation, materials, raw material, test methods and related technical data.
Perform data profiling and quality checks to identify missing information, inconsistencies, duplicate records, incorrect attributes and deviations from agreed standards.
Review formulation and materials records to assess completeness, consistency and accuracy against technical requirements and established data definitions.
Identify data anomalies and investigate discrepancies in formulation information, material properties, test results and associated records.
Develop and maintain data quality rules, validation checks and monitoring processes.
Track data quality metrics and identify recurring issues and opportunities for improvement.
2. Data Cleansing and Remediation
Coordinate data clean-up and remediation activities to improve the quality and consistency of existing datasets.
Work closely with formulation scientists, laboratory personnel, materials specialists and data owners to investigate and resolve data discrepancies.
Validate corrections with relevant subject-matter experts to ensure that changes are scientifically appropriate and accurately reflect the underlying technical information.
Identify root causes of recurring data quality issues and recommend preventive measures.
Maintain clear records of identified issues, corrective actions, decisions and resolution status.
Follow up with stakeholders to ensure that outstanding data issues are resolved in a timely manner.
3. Scientific and Technical Data understanding
Apply knowledge of chemistry, materials science and laboratory practices to understand the context and meaning of technical data.
Understand how formulations are developed, tested, evaluated and documented in a laboratory or R&D environment.
Review formulation-related information, including raw materials, ingredients, compositions, material properties, test parameters and results, where applicable to the organisation's data.
Engage with technical experts to clarify data definitions, resolve ambiguities and establish appropriate data quality requirements.
Help distinguish genuine data quality issues from legitimate technical variations in formulations, materials or testing conditions.
Support the development of consistent terminology, data definitions and standards for scientific and technical information.
4. Data governance and standardization
Support the establishment and maintenance of data standards, validation rules and governance practices for formulation and materials data.
Improve consistency in the way technical information is captured, classified, named, structured and maintained.
Work with data owners and business stakeholders to clarify ownership, responsibilities and processes for maintaining data quality.
Document data definitions, business rules, quality requirements and standard operating procedures.
Promote consistent data management practices across relevant laboratory, R&D and business teams.
Identify opportunities to strengthen data controls and improve the reliability of data at the point of creation.
5. Stakeholder collaboration and user support
Act as a key contact for data quality issues relating to formulation, materials and laboratory information.
Work directly with users to understand data-related challenges, clarify requirements and agree on corrective actions.
Collaborate with laboratory teams, formulation scientists, R&D, quality and IT or data management teams to resolve issues.
Support users in understanding data standards and maintaining accurate and complete records.
Communicate findings and recommendations clearly to both technical and non-technical stakeholders.
Encourage accountability for data quality and promote continuous improvement in data management practices.
6. Reporting and continuous improvement
Prepare reports and dashboards to communicate data quality performance, outstanding issues and remediation progress.
Monitor trends in data quality and identify areas requiring further investigation or corrective action.
Recommend improvements to data capture, validation, maintenance and review processes.
Support initiatives to improve the accessibility, consistency and usability of formulation and materials data.
Contribute to the continuous improvement of data quality processes and practices across the organisation.
Qualifications
Degree in Chemistry, Materials Science, Chemical Engineering, Polymer Science or a related scientific or engineering discipline.
Strong practical background in a chemistry or materials science laboratory, with particular experience in formulation development, materials testing, product development or a related R&D environment.
Good understanding of how formulations and materials are developed, tested, characterised and documented.
Experience working with technical or scientific data, including reviewing, validating, analysing or maintaining formulation, material or laboratory records.
Strong analytical skills and the ability to identify inconsistencies, investigate discrepancies and assess data quality systematically.
Ability to work directly with laboratory users, scientists and technical stakeholders to clarify information and resolve data issues.
Strong attention to detail, organisational skills and a structured approach to problem-solving.
Clear communication skills and the ability to work collaboratively across scientific, technical and business functions.
A proactive mindset with the ability to follow through on issues and drive improvements to completion.
Preferred Qualifications
Experience in formulation development or testing involving chemicals, polymers, coatings, adhesives, specialty materials or other formulated products.
Experience managing, reviewing or cleaning technical datasets, laboratory records, product specifications or formulation databases.
Familiarity with laboratory information management systems (LIMS), electronic laboratory notebooks (ELN), product lifecycle management (PLM), ERP systems or materials management platforms.
Experience with Excel, SQL, Power BI or similar tools for data profiling, analysis, validation and reporting.
Exposure to data governance, master data management, data stewardship or formal data quality processes.
Experience working with R&D, product development, analytical testing, quality control or technical service teams.

Work arrangement
Hybrid