TetraScience
Senior PreSales Solutions Architect(m/w/x)
Vollzeitmit HomeofficeSenior
Basel
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TETetraScience
Scientific Data Architect(m/w/x)
Basel
Vollzeitmit HomeofficeBerufserfahren
AI/ML
Nejo KI-Zusammenfassung
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.
Anforderungen
- 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
Aufgaben
- 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
Berufserfahrung
- 4 Jahre
Ausbildung
- Master-Abschluss
Sprachen
- Englisch – verhandlungssicher
Benefits
Attraktive Vergütung
- Company equity
Lockere Unternehmenskultur
- Supportive team culture
Mehr Urlaubstage
- Generous paid time off
Flexibles Arbeiten
- Flexible working arrangements
- Remote work
Die Originalanzeige dieses Stellenangebotes in der aktuellsten Version findest du hier. Nejo hat diesen Job automatisch von der Website des Unternehmens TetraScience erfasst und die Informationen auf Nejo mit Hilfe von KI für dich aufbereitet. Trotz sorgfältiger Analyse können einzelne Informationen unvollständig oder ungenau sein. Bitte prüfe immer alle Angaben in der Originalanzeige! Inhalte und Urheberrechte der Originalanzeige liegen beim ausschreibenden Unternehmen.
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TETetraScience
Scientific Data Architect(m/w/x)
Basel
Vollzeitmit HomeofficeBerufserfahren
AI/ML
Nejo KI-Zusammenfassung
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.
Anforderungen
- 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
Aufgaben
- 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
Berufserfahrung
- 4 Jahre
Ausbildung
- Master-Abschluss
Sprachen
- Englisch – verhandlungssicher
Benefits
Attraktive Vergütung
- Company equity
Lockere Unternehmenskultur
- Supportive team culture
Mehr Urlaubstage
- Generous paid time off
Flexibles Arbeiten
- Flexible working arrangements
- Remote work
Die Originalanzeige dieses Stellenangebotes in der aktuellsten Version findest du hier. Nejo hat diesen Job automatisch von der Website des Unternehmens TetraScience erfasst und die Informationen auf Nejo mit Hilfe von KI für dich aufbereitet. Trotz sorgfältiger Analyse können einzelne Informationen unvollständig oder ungenau sein. Bitte prüfe immer alle Angaben in der Originalanzeige! Inhalte und Urheberrechte der Originalanzeige liegen beim ausschreibenden Unternehmen.
Über das Unternehmen
TetraScience
Branche
Science
Beschreibung
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.
Noch nicht perfekt?
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