TetraScience
Senior PreSales Solutions Architect(m/w/x)
Full-timeWith HomeofficeSenior
Basel
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TETetraScience
Scientific Data Architect(m/w/x)
Basel
Full-timeWith Home OfficeExperienced
AI/ML
Nejo AI Summary
Designing and implementing extensible data models for AI-native scientific data sets in life sciences. PhD with 4+ years or Masters with 8+ years industry experience in drug discovery, preclinical, CMC, or quality testing required. Company equity and generous paid time off.
Requirements
- PhD with +4 years or Masters with +8 years industry experience in life sciences
- Extensive domain knowledge in drug discovery
- Extensive domain knowledge in preclinical development
- Extensive domain knowledge in CMC (all drug modalities)
- Extensive domain knowledge in product quality testing
- Proven track record of defining AI/ML use cases
- Proven track record of designing AI/ML use cases
- Proven track record of prototyping AI/ML use cases
- Proven track record of implementing AI/ML use cases
- Productized AI/ML-driven use cases in cloud environments
- Collaboration with cross-functional teams
- Collaboration with product managers
- Collaboration with software engineers
- Collaboration with scientific stakeholders
- Extensive exploratory data analysis
- Workflow optimization for scientific outcomes
- Engaging diverse audiences
- Excellent communication abilities
- Excellent storytelling abilities
- Consulting scientists for research outcomes
- Consulting scientists for development outcomes
- Consulting scientists for quality testing outcomes
- Product-minded driver of technical scientific solutions
- Outcome-obsessed driver of technical scientific solutions
- High velocity self-starter
- Refusal to let uncertainty obstruct path to solutions
- Rolling up sleeves and trying things out
- Getting things done
- Prototyping to accelerate delivery
- Demonstrating to accelerate delivery
- Building to accelerate delivery
- Thriving in collaborative environments
- Transforming complex scientific data into actionable outcomes
- Engaging scientists and business leaders
- Applying cutting edge data methodologies to biopharma R&D
- Bridging understanding between pain points and solutions
- Insatiable learner
- Deep learning of new tools
- Deep learning of new methods
- Deep learning of new domains
- Embodying principles of extreme ownership
- Building extensible data models for Biopharma
- Building extensible applications for Biopharma
- Maximizing value from data via analysis
- Maximizing value from data via AI/ML integration
- Extreme self-discipline
- Determination to forge new categories
Tasks
- Engage directly with customers onsite 4-5 days per week
- Build strong customer relationships
- Understand customer scientific data challenges and requirements
- Accelerate customer solutions
- Design and implement extensible, reusable data models
- Capture and organize scientific data for use cases
- Ensure data model scalability and future adaptability
- Translate scientific data workflows into solutions
- Leverage the Tetra Data Platform
- Own, scope, prototype, and implement solutions
- Design data models (tabular & JSON)
- Develop Python-based parsers
- Integrate lab software (e.g., ELN/LIMS) via APIs
- Develop data visualizations and apps in Python
- Use app frameworks like Streamlit
- Use plotting tools like holoviews and Plotly
- Collaborate with Scientific Business Analysts
- Collaborate with customer scientists
- Collaborate with applied AI engineers
- Develop and deploy models (ML, AI, mechanistic, statistical, hybrid)
- Programmatically interrogate proprietary instrument output files
- Iterate with scientific end users and technical stakeholders
- Drive solution development and adoption
- Conduct regular demos and meetings
- Communicate implementation progress proactively
- Deliver demos to customer stakeholders
- Collaborate with the product team
- Build and prioritize the product roadmap
- Understand customer pain points within and outside Tetra Data Platform
- Rapidly learn new technologies
- Develop and troubleshoot use cases
Work Experience
- 4 years
Education
- Master's degree
Languages
- English – Business Fluent
Benefits
Competitive Pay
- Company equity
Informal Culture
- Supportive team culture
More Vacation Days
- Generous paid time off
Flexible Working
- Flexible working arrangements
- Remote work
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of TetraScience and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
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TETetraScience
Scientific Data Architect(m/w/x)
Basel
Full-timeWith Home OfficeExperienced
AI/ML
Nejo AI Summary
Designing and implementing extensible data models for AI-native scientific data sets in life sciences. PhD with 4+ years or Masters with 8+ years industry experience in drug discovery, preclinical, CMC, or quality testing required. Company equity and generous paid time off.
Requirements
- PhD with +4 years or Masters with +8 years industry experience in life sciences
- Extensive domain knowledge in drug discovery
- Extensive domain knowledge in preclinical development
- Extensive domain knowledge in CMC (all drug modalities)
- Extensive domain knowledge in product quality testing
- Proven track record of defining AI/ML use cases
- Proven track record of designing AI/ML use cases
- Proven track record of prototyping AI/ML use cases
- Proven track record of implementing AI/ML use cases
- Productized AI/ML-driven use cases in cloud environments
- Collaboration with cross-functional teams
- Collaboration with product managers
- Collaboration with software engineers
- Collaboration with scientific stakeholders
- Extensive exploratory data analysis
- Workflow optimization for scientific outcomes
- Engaging diverse audiences
- Excellent communication abilities
- Excellent storytelling abilities
- Consulting scientists for research outcomes
- Consulting scientists for development outcomes
- Consulting scientists for quality testing outcomes
- Product-minded driver of technical scientific solutions
- Outcome-obsessed driver of technical scientific solutions
- High velocity self-starter
- Refusal to let uncertainty obstruct path to solutions
- Rolling up sleeves and trying things out
- Getting things done
- Prototyping to accelerate delivery
- Demonstrating to accelerate delivery
- Building to accelerate delivery
- Thriving in collaborative environments
- Transforming complex scientific data into actionable outcomes
- Engaging scientists and business leaders
- Applying cutting edge data methodologies to biopharma R&D
- Bridging understanding between pain points and solutions
- Insatiable learner
- Deep learning of new tools
- Deep learning of new methods
- Deep learning of new domains
- Embodying principles of extreme ownership
- Building extensible data models for Biopharma
- Building extensible applications for Biopharma
- Maximizing value from data via analysis
- Maximizing value from data via AI/ML integration
- Extreme self-discipline
- Determination to forge new categories
Tasks
- Engage directly with customers onsite 4-5 days per week
- Build strong customer relationships
- Understand customer scientific data challenges and requirements
- Accelerate customer solutions
- Design and implement extensible, reusable data models
- Capture and organize scientific data for use cases
- Ensure data model scalability and future adaptability
- Translate scientific data workflows into solutions
- Leverage the Tetra Data Platform
- Own, scope, prototype, and implement solutions
- Design data models (tabular & JSON)
- Develop Python-based parsers
- Integrate lab software (e.g., ELN/LIMS) via APIs
- Develop data visualizations and apps in Python
- Use app frameworks like Streamlit
- Use plotting tools like holoviews and Plotly
- Collaborate with Scientific Business Analysts
- Collaborate with customer scientists
- Collaborate with applied AI engineers
- Develop and deploy models (ML, AI, mechanistic, statistical, hybrid)
- Programmatically interrogate proprietary instrument output files
- Iterate with scientific end users and technical stakeholders
- Drive solution development and adoption
- Conduct regular demos and meetings
- Communicate implementation progress proactively
- Deliver demos to customer stakeholders
- Collaborate with the product team
- Build and prioritize the product roadmap
- Understand customer pain points within and outside Tetra Data Platform
- Rapidly learn new technologies
- Develop and troubleshoot use cases
Work Experience
- 4 years
Education
- Master's degree
Languages
- English – Business Fluent
Benefits
Competitive Pay
- Company equity
Informal Culture
- Supportive team culture
More Vacation Days
- Generous paid time off
Flexible Working
- Flexible working arrangements
- Remote work
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of TetraScience and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
About the Company
TetraScience
Industry
Science
Description
TetraScience is the Scientific Data and AI company, catalyzing the Scientific AI revolution by designing and industrializing AI-native scientific data sets for lab data management solutions and AI-enabled outcomes.
Not a perfect match?
- TetraScience
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