Data Modeler

30+ days ago

Job Description

Data Modeler
Key Responsibilities:

Develop and maintain conceptual and logical information models for scientific and business domains relevant to R&D and Development.
Translate business, scientific, and operational requirements into clear information structures, definitions, and business rules.
Support the definition and application of information architecture frameworks, standards, and best practices across relevant domains.
Help ensure information assets are designed to be reusable, scalable, and aligned to business and scientific needs.
Contribute to the creation and maintenance of data dictionaries, glossaries, naming standards, controlled vocabularies, and taxonomies.

Data Exploration and Assessment

Profile and analyze source data to understand quality, completeness, context, and usability.
Identify gaps, inconsistencies, missing metadata, and alignment issues across data sources.
Assess the fitness of source data for intended use cases and downstream consumption.
Summarize findings from data exploration and recommend pragmatic remediation or standardization actions.
Support root-cause analysis of data quality and information consistency issues.

Collaboration and Stakeholder Engagement

Partner with scientists, subject matter experts, business stakeholders, and IT teams to understand workflows, priorities, and information needs.
Work collaboratively with data engineers, architects, and platform teams to ensure models and standards are implementable.
Facilitate workshops and review sessions to validate models, standards, and requirements.
Communicate clearly with technical and non-technical audiences, including documentation of models, diagrams, findings, and recommendations.
Act as a connector between scientific users and technical implementation teams.

Standards, Governance, and Enablement

Contribute to the development and maintenance of information quality rules and governance-aligned standards.
Support documentation and stewardship of information architecture deliverables.
Help enable reuse through clear definitions, standards, and supporting guidance.
Promote consistent application of FAIR principles and GxP-aware practices where relevant.
Identify opportunities to improve tooling, methods, and ways of working related to information architecture.

Required Experience and Skills

Strong scientific background, preferably in Chemistry, Biology, or another scientific field related discipline
Demonstrated advanced problem-solving and analytical skills.
Experience working with or within IT teams in a collaborative environment.
Strong communication skills, with the ability to work effectively across scientific, business, and technical audiences.
Proven ability to manage ambiguity, think structurally, and learn new methods quickly.
Strong attention to detail and commitment to quality.
Ability and willingness to learn information architecture, data modeling, metadata, and data standards.

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