Scientific Data Architect

About the role

• Engage directly with customers onsite a couple of days per week in the Frankfurt Region • Build strong customer relationships and understand scientific data challenges and requirements • Design and implement extensible, reusable data models for scientific use cases • Translate scientific data workflows into robust solutions using the Tetra Data Platform • Own, scope, prototype, and implement data and AI/ML solutions • Develop Python-based parsers • Integrate lab software such as ELN/LIMS via APIs • Build data visualizations and applications in Python using Streamlit, holoview…

Requirements

• • PhD with +4 years or Masters with +8 years of industry experience in life sciences
• • 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
• • Extensive exploratory data analysis and workflow optimization experience
• • Excellent communication and storytelling abilities for engaging scientists through executive stakeholders
• • Consulting experience advising scientists to advance research, development, and quality testing outcomes
• • Experience designing data models (tabular and JSON)
• • Python-based parser development skills
• • Lab software integration via APIs, including ELN/LIMS
• • Python data visualization and app development experience, including Streamlit, holoviews, and Plotly
• • Experience collaborating with scientific business analysts, customer scientists, and applied AI engineers

What you'll do

• • Engage directly with customers onsite a couple of days per week in the Frankfurt Region
• • Build strong customer relationships and understand scientific data challenges and requirements
• • Design and implement extensible, reusable data models for scientific use cases
• • Translate scientific data workflows into robust solutions using the Tetra Data Platform
• • Own, scope, prototype, and implement data and AI/ML solutions
• • Develop Python-based parsers
• • Integrate lab software such as ELN/LIMS via APIs
• • Build data visualizations and applications in Python using Streamlit, holoviews, and Plotly
• • Collaborate with Scientific Business Analysts, customer scientists, and applied AI engineers to develop and deploy models
• • Interrogate proprietary instrument output files programmatically
• • Iterate with scientific end users and technical stakeholders through demos and meetings to drive adoption
• • Communicate implementation progress and deliver demos to customer stakeholders

Where this was posted

• remoterocketship.com

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