Senior Scientific Data Architect – Remote & AI-Driven

TetraScience; København; On-site; DKK 900,000 - 1,200,000; Full time

Benefits offered by this job

Equity

Generous PTO

Remote work option

Job summary

TetraScience is seeking a product-minded AI/ML leader to industrialize Scientific AI for life sciences. You will engage customers onsite up to 25%, translate complex scientific workflows into scalable data models, and prototype solutions using the Tetra Data Platform.

You will collaborate with scientists, PMs and engineers to deploy models, enhance data tooling, and accelerate discovery. Strong communication and continuous learning are essential.

Qualifications

• PhD with 4+ years or Masters with 8+ years in life sciences.
• Proven AI/ML use-case productization in cloud environments.
• Collaborated with cross-functional teams of scientists, PMs and engineers.
• Extensive data analysis and workflow optimization for scientific outcomes.
• Excellent communication with scientists and executives.

Responsibilities

• Be a key member in industrializing Scientific AI, traveling up to 25%.
• Design extensible data models to capture scientific data at scale.
• Translate workflows into robust solutions with the Tetra Data Platform.
• Prototype and implement data models, Python parsers, and ELN/LIMS APIs.
• Collaborate with SBAs and engineers to deploy ML/AI models.
• Demos and updates to customer stakeholders; influence roadmap.
• Continuously learn new technologies to support use cases.

Skills

AI/ML development

Python

Cloud environments

Data modeling

Education

PhD +4 years life sciences

Masters +8 years life sciences

Tools

Streamlit

Plotly

Holoviews

Python tooling

Job description

TetraScience is seeking a product-minded AI/ML leader to industrialize Scientific AI for life sciences. You will engage customers onsite up to 25%, translate complex scientific workflows into scalable data models, and prototype solutions using the Tetra Data Platform.

You will collaborate with scientists, PMs and engineers to deploy models, enhance data tooling, and accelerate discovery. Strong communication and continuous learning are essential.