Scientific Data Architect -Darmstadt or Düsseldorf, Germany
About TetraScience
TetraScience is the Scientific Data and AI Company building Tetra OS, the operating system for scientific intelligence. We help the world’s leading life sciences firms turn fragmented scientific data into AI-native assets and scientific workflows that accelerate discovery, development, and manufacturing. TetraScience’s growing ecosystem of strategic partners includes NVIDIA, Databricks, Thermo Fisher Scientific, Snowflake, Google, and Microsoft.
In connection with your candidacy, you will be asked to carefully review “The Tetra Way,” authored by our CEO, Patrick Grady; it is impossible to overstate the importance of this document, and you should take it literally as you decide whether our mission, culture, and expectations are right for you.
Who You Are
You are a product-minded, outcome-obsessed driver of technical scientific solutions.
You a high velocity self-starter. You refuse to let uncertainty obstruct your path to designing and building solutions.
You roll up your sleeves, try things out, and get things done. You do not hesitate to prototype, demo, and build in order to accelerate delivery of products for your end users.
You thrive in environments where you can collaborate with scientists, product managers, and engineers to transform complex scientific data into actionable outcomes. Your ability to engage with scientists and business leaders alike makes you a key player in maximizing the value of scientific data.
With rich experience applying cutting edge data methodologies to the biopharma R&D domain, you bridge understanding between present-day pain points and generalizable solutions.
You are an insatiable learner, with a track record of deeply learning new tools, methods, and domains.
You fundamentally embody the principles of extreme ownership and have a demonstrated history of building extensible data models and applications for Biopharma end users to maximize value from their data via analysis and integration with AI/ML.
This role will require extreme self-discipline and determination as we forge a category that will fundamentally and forever change the life science industry.
What You Have Done
- PhD with +4 years or Masters with +8 years of industry experience in life sciences with 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
- Collaborated with cross-functional teams, including product managers, software engineers, and scientific stakeholders.
- Performed extensive exploratory data analysis and workflow optimization to enable scientific outcomes not previously possible.
- Engaged diverse audiences, from scientists to executive stakeholders using your excellent communication and storytelling abilities.
- Advised scientists in a consulting capacity to further research, development, and quality testing outcomes.
Requirements
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 a couple of days per week in the Frankfurt Region, building strong relationships, deeply understanding their scientific data challenges and requirements, and accelerating solutions.
- Design and implement extensible, reusable data models that efficiently capture and organize scientific data for scientific use cases, ensuring scalability and future adaptability.
- Translate scientific data workflows into robust solutions leveraging the Tetra Data Platform.
- Own, scope, prototype, and implement solutions including:
- Data model design (tabular & JSON)
- Python-based parser development.
- Lab software (e.g., ELN/LIMS) integration via APIs.
- Data visualization and app development in Python (using app frameworks like Streamlit and plotting tools like holoviews and Plotly)
- Collaborate with Scientific Business Analysts (SBAs), customer scientists and applied AI engineers to develop and deploy models (ML, AI, mechanistic, statistical, hybrid)
- Programmatically interrogating proprietary instrument output files.
- Dynamically iterate with scientific end users and technical stakeholders to rapidly drive solution development and adoption through regular demos and meetings
- Proactively communicate implementation progress and deliver demos to customer stakeholders.
- Collaborate with the product team to build and prioritize our roadmap by understanding customers’ pain points within and outside Tetra Data Platform.
- Rapidly learn new technologies (e.g., new AWS services or scientific analysis applications) to develop and troubleshoot use cases
- Business proficiency of German required (C1 level)
Benefits
- Competitive Salary and equity in a fast-growing company.
- Supportive, team-oriented culture of continuous improvement.
- Generous paid time off (PTO).
- Flexible working arrangements - Remote work when not at Customer Sites
We are not currently providing visa sponsorship for this position.
Skills
As published by workable · 13 questions · 7 written answers
Basics
First name, Last name, Email, Headline, Phone, Address, Photo, Education, Experience, Summary, Resume, Cover letter
Short answers (2)
- How are you a cultural fit for a fast-moving, semi-virtual start-up?
- Please describe how you learn new things; what are you curious about?
Pick from a list (4)
- Do you have a PhD with 7+ years or a Masters with 10+ years of industry experience in life sciences?
- Do you have a proven track record of defining, designing, prototyping, and implementing productized AI/ML-driven use cases in cloud environments?
- Are you able to commute to Frankfurt for customers onsites a few days a week?
- Do you have a background in biology, chemistry, bioengineering, materials science, or some other pharma/biotech relevant field?
Written answers (7)
- Project spotlight – end-to-end delivery Describe a project where you delivered a technical solution for scientific users. State the scientific workflow outcome you aimed to enable, list the main systems or data sources you integrated, and highlight one design decision that made the biggest difference to the result.
- When a tech roll-out failed Tell us about a technology you (or a team you worked with) introduced that scientists ultimately rejected. Explain the need the tool was meant to address, give two specific reasons adoption failed, and outline one different tactic you would try if you could start over.
- Tough stakeholder moment Write about your most challenging interaction with a customer or internal stakeholder. Summarize the situation and the competing interests, explain two concrete actions you took to resolve it, and state one lesson you now apply elsewhere.
- Rapid prototyping tools List up to three tools or frameworks you have adopted in the last 18 months to test ideas or build MVPs quickly. For each, give a one-sentence example of how it sped up decision-making.
- Biggest data-modeling challenge Describe the most daunting data-modeling problem you have faced. Explain why it was difficult, highlight the hardest technical hurdle and the hardest scientist-workflow hurdle, and note the key lesson you learned that you now reuse on other projects.
- Advocate for a solution Describe two situations where you have to convince and evangelize to scientists to adopt your solution and convince technical audience to not build solutions themselves. For each, give specific examples on what has worked and what has not (what you would do more vs would do less next time).
- Start up Provide concrete examples of you fitting and thriving in a fast-pace, high urgency, start up environment.