Scientific Business Analyst- Darmstadt or Düsseldorf, Germany

Stellenbeschreibung:

Scientific Business Analyst - Frankfurt, Germany

Be among the first 25 applicants.

Who We Are

TetraScience is the Scientific Data and AI company. We are catalyzing the Scientific AI revolution by designing and industrializing AI-native scientific data sets, which we bring to life in a growing suite of next‑gen lab data management solutions, scientific use cases, and AI‑enabled outcomes.

TetraScience is the category leader in this vital new market, generating more revenue than all other companies in the aggregate. In the last year alone, the world’s dominant players in compute, cloud, data, and AI infrastructure have converged on TetraScience as the de‑facto standard, entering into co‑innovation and go‑to‑market partnerships.

In connection with your candidacy, you will be asked to carefully review the Tetra Way letter, authored directly by Patrick Grady, our co‑founder and CEO. This letter is designed to assist you in better understanding whether TetraScience is the right fit for you from a values and ethos perspective.

It is impossible to overstate the importance of this document and you are encouraged to take it literally and reflect on whether you are aligned with our unique approach to company and team building. If you join us, you will be expected to embody its contents each day.

Who You Are

You are a strategic, analytically minded professional with a passion for bridging scientific insights and cutting‑edge technology. You thrive in environments where you can collaborate with scientists, product managers, and engineers to transform complex scientific data into actionable outcomes.

With deep domain knowledge in drug discovery/preclinical development, CMC, or Quality , you are skilled at uncovering innovative use cases that drive AI and machine learning applications. Your ability to engage with scientists and business leaders alike makes you a key player in maximizing the value of scientific data.

You will need to be a high clock‑speed and forward‑thinking individual with a passion for developing requirements for complex solutions targeted to R&D and Quality personas inside of Life Sciences. You embody extreme ownership and have a demonstrated history of deriving maximum value from data through enrichment, analysis, and integration with AI and machine learning applications.

You should also be energized by regularly working onsite with customers . You thrive in dynamic, high‑impact, face‑to‑face collaborative environments where you can build deep relationships and drive scientific transformation firsthand.

Requirements

What You Have Done

PhD with 15+ years of industry experience in life sciences, preferably across pharma, biotech, or health tech, with deep domain expertise in discovery, preclinical, CMC, and/or Quality
Extensive hands‑on experience or direct oversight in one or more of the following areas: high throughput screening, preclinical toxicology, materials engineering, analytical development, drug substance (DS) synthesis and manufacturing
Delivered requirements for AI/ML‑driven solutions in operational or productized environments that improved efficiency, reduced cost, and enhanced data utilization
Extensive hands‑on experience with scientific data workflows and lab automation; exposure to FAIR principles and modern data architecture is a plus
Strong coding or scripting background (e.g., Python, Nextflow, AWS, SDKs) and familiarity with scientific tools, databases, and ontologies is preferred
Exceptional communication and storytelling ability to engage technical and executive stakeholders
Prior experience in customer‑facing, consulting, or commercial‑scientific interface roles

What You Will Do

You will be a critical team member in a unique partnership to industrialize Scientific AI. As such, you will engage directly with customers onsite up to 4-5 days per week in the Frankfurt Region.
Customer Data Exploration: Investigate diverse customer datasets, identifying enrichment and AI‑readiness opportunities
Scientific Use Case Development: Collaborate with customers to define, iterate, and implement innovative scientific AI/ML use cases
Stakeholder Engagement: Conduct onsite interviews and workshops to deeply understand customer challenges and data landscapes
Data Analysis and Enrichment: Perform exploratory data analysis and define transformation workflows that enable scientific AI
Workflow Documentation: Develop visual documentation including workflow diagrams, ERDs, and ontology definitions
AI Model Evaluation: Provide practical scientific input on model output, with suggestions to improve real‑world performance
Customer Enablement: Deliver onsite demonstrations, conduct working sessions, and act as a trusted advisor in AI adoption
Strategic Insight: Propose new directions, experiments, or platforms that can amplify scientific discovery and development