Scientific Data Architect

TetraScience

. Engage directly with customers onsite, build strong relationships, understand scientific data challenges and requirements, and accelerate solutions.

Posted 9/22/2026full-timeCopenhagen • DenmarkMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing extensible data models for scientific applications, leveraging Python for data parsing and visualization. Proven ability to collaborate with cross-functional teams to develop AI/ML-driven solutions in life sciences, particularly in drug discovery and preclinical development.

Highest-signal resume keywords

PhD In Life SciencesPython-Based Parser DevelopmentData Visualization In PythonExperience With Tetra Data PlatformCollaboration With Scientific Stakeholders

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Model DesignScientific Data WorkflowsExploratory Data AnalysisAI/ML Use Case ImplementationIntegration Of Lab Software Via APIs

Soft Skills

Excellent CommunicationStorytelling AbilitiesCustomer Engagement

Tools & Technologies

StreamlitHoloviewsPlotlyAWS Services

Industry Keywords

Drug DiscoveryPreclinical DevelopmentCMCProduct Quality Testing

Tech Stack

Tools & technologies

AWSCloudPython

About the role

Key responsibilities & impact

Engage directly with customers onsite, build strong relationships, understand scientific data challenges and requirements, and accelerate solutions.
Design and implement extensible, reusable data models that capture and organize scientific data for scalability and future adaptability.
Translate scientific data workflows into robust solutions using the Tetra Data Platform.
Own, scope, prototype, and implement data model designs in tabular and JSON formats.
Develop Python-based parsers.
Integrate lab software such as ELN/LIMS via APIs.
Develop data visualizations and applications in Python using frameworks such as Streamlit and tools such as holoviews and Plotly.
Collaborate with Scientific Business Analysts, customer scientists, and applied AI engineers to develop and deploy ML, AI, mechanistic, statistical, and hybrid models.
Programmatically interrogate proprietary instrument output files.
Iterate with scientific end users and technical stakeholders through demos and meetings to drive solution development and adoption.
Communicate implementation progress and deliver demos to customer stakeholders.
Collaborate with the product team to build and prioritize the roadmap based on customer pain points.
Learn new technologies and troubleshoot use cases.

Requirements

What you’ll need

PhD with +4 years or Masters with +8 years of industry experience in life sciences, with extensive domain knowledge in drug discovery (target ID through lead optimization), preclinical development, CMC (all drug modalities), or product quality testing.
Proven track record of defining, designing, prototyping, and implementing productized AI/ML-driven use cases in cloud environments.
Experience collaborating with product managers, software engineers, and scientific stakeholders.
Experience performing extensive exploratory data analysis and workflow optimization to enable scientific outcomes not previously possible.
Excellent communication and storytelling abilities for engaging scientists through executive stakeholders.
Experience advising scientists in a consulting capacity to further research, development, and quality testing outcomes.
Ability to engage directly with customers onsite and travel up to 25% of the time.
Ability to design and implement extensible, reusable data models for scientific use cases.
Ability to translate scientific data workflows into robust solutions leveraging the Tetra Data Platform.
Python-based parser development.
Lab software (e.g., ELN/LIMS) integration via APIs.
Data visualization and app development in Python using frameworks such as Streamlit and tools such as holoviews and Plotly.
Ability to collaborate with Scientific Business Analysts, customer scientists, and applied AI engineers to develop and deploy ML, AI, mechanistic, statistical, and hybrid models.
Experience programmatically interrogating proprietary instrument output files.
Ability to rapidly learn new technologies, such as AWS services or scientific analysis applications.
Visa sponsorship is not currently provided for this position.

Benefits

Comp & perks

Competitive Salary and equity in a fast-growing company.
Supportive, team-oriented culture of continuous improvement.
Generous paid time off (PTO).
Flexible working arrangements - Remote work when not at Customer Sites