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TetraScience

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Scientific Data Architect - Tarrytown, NY

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 are 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.

Requirements

What You Have Done

You deeply understand the life science R&D data ecosystem – you’ve felt the pain of brittle, bespoke workflows with fractured data, and you’ve actively worked to solve this. In our experience, the candidates with this experience bring the following background:

  • PhD with +4 years, Masters with +6 years, or Bachelors 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), product quality testing, or pharma manufacturing.
  • Proven track record of defining, designing, prototyping, and implementing productized AI/ML-driven use cases in cloud environments
  • Designed scalable and reusable data architecture
  • 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.
  • Engage 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.
  • Augmented your technical, business, and communication work through agentic development and knowledge work
  • Nice to have: Hybrid dry lab / wet lab experience

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 assigned geographic 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
    • Python-based pipeline development.
    • Lab software (e.g., ELN/LIMS) integration via APIs.
    • Data visualization and app development
    • Scientific agents
  • Leverage agentic development tools like Claude Code and Codex to contribute to discovery, prototyping, and development
  • Collaborate with Scientific Business Analysts (SBAs), customer scientists and applied AI engineers to develop and deploy models (ML, AI, mechanistic, statistical, hybrid) and agents
  • Interface directly 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 to develop and troubleshoot use cases
  • Must be able to travel to client sites in local geographic areas

Benefits

  • 100% employer-paid benefits for all eligible employees and immediate family members
  • Unlimited paid time off (PTO)
  • 401K
  • Company paid Life Insurance, LTD/STD
  • A culture of continuous improvement where you can grow your career and get coaching

We are not currently providing visa sponsorship for this position.

The salary range for this position is $140,000 - $240,000. The salary range posted reflects our target baseline for this role. Final compensation is determined by a thorough evaluation of factors including the candidate’s specific experience, localized market data, and internal team equity.

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What this application asks

workable

First name, Last name, Email, Headline, Phone, Address, Education, Experience, Summary, Resume, Cover letter

  • Do you have a proven track record of defining, designing, prototyping, and implementing productized AI/ML-driven use cases in cloud environments? yes / no
  • Are you willing/capable of working with customers onsite in Tarrytown, NY? yes / no
  • 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?
  • 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. written answer
  • 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. written answer
  • 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. written answer
  • 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. written answer
  • 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. written answer
  • 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). written answer
  • Start up Provide concrete examples of you fitting and thriving in a fast-pace, high urgency, start up environment. written answer
  • Are you authorized to work in the United States? yes / no
  • Do you now or in the future require visa sponsorship to continue working in the United States? yes / no

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