Data Modeler

TechDigital Group; Pennsylvania; On-site; USD 70,000 - 90,000; Full time

Job summary

TechDigital Group is seeking a Data Modeler based in West Point, Pennsylvania. In this role, you will develop and maintain information models relevant to R&D and work with scientific and technical teams to ensure alignment and quality of data.

The ideal candidate will possess a strong scientific background, particularly in Chemistry or Biology, and have demonstrated analytical and problem-solving skills. This position offers a unique opportunity to shape data architecture within the organization.

Qualifications

• Strong scientific background related to Chemistry, Biology, or similar.
• Demonstrated analytical skills and problem-solving abilities.
• Ability to communicate effectively with diverse audiences.

Responsibilities

• Develop and maintain information models for R&D and Development.
• Profile and analyze data to assess quality and usability.
• Collaborate with stakeholders to gather and understand information needs.

Skills

Scientific background in Chemistry, Biology, or related fields

Problem-solving and analytical skills

Collaboration with IT teams

Strong communication skills

Attention to detail

Willingness to learn information architecture

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.