TetraScience Sciborgs Drive Better Science

February 22, 2023
Ken Fountain

Consider cyborgs - humans enhanced with tech components. They’re omnipresent: Arnold Schwarzenegger as the T-100 in Terminator. Steve Austin, saving the world as the $6,000,000 man. Jean-Luc Picard as Locutus of Borg, the biggest threat to Star Trek’s Federation. Genos, the wandering apprentice from anime series One-Punch Man.

The fusion of technology, data, and science to meld the biological to the technological represents raw, synergistic power: both leading to a 1+1+1 = 5 type of outcome. A true differentiation for those who’d harness this type of hybrid individual. For others thinking about Sciborgs, check out the recent post by Jorge Conde from a16z[1], in which he discusses the biopharma SaaS landscape and refers to ‘multi-lingual founders fluent in both tech and biotech.

Already in 1980, Steve Jobs hypothesized[2] that human augmentation would radically increase problem-solving and usher in a new era: “Computers are like ‘bicycles for the mind’; they take us far beyond our inherent abilities. And I think we’re just at the very early stages of this tool: we’ve come a very short distance, and it’s still in its formation, but that’s nothing compared to what we’ll see in the coming years.”

TetraScience sees that very same emergence four decades later. Our broad and ever-growing customer and partner networks evince the same challenges – achieve orders of magnitude increases in:

  • Speed(of assays, of screens, of experiments)
  • Quality(risk reduction and improved GxP compliance)
  • Impact(making critical, data-informed decisions to expedite therapeutics)

Our perspective? These challenges require the best of human creativity, paired with technical innovation. Biopharmaceutical companies must migrate their workflows to the Cloud so it “flows” across different scientific processes and across global organizations. This movement also aligns with initiatives across the pre-competitive and thought leader space around standardization and harmonization of primary data sets.

Palantir, who serves predominantly the defense and Big Tech spaces, deploys FDSEs - forward-deployed software engineers - whose day-to-day life was elucidated in this 2020 blog post.[3] They design, build, and test prototypic workflows, juggle multiple workflows, and deeply intuit and understand customer needs. But this expertise does not extend to biopharma - the gap persists.

Here at TetraScience, we were inspired by the writings of Susan Schneider, who when interviewed by Scientific American in 2020[4]discussed a hypothetical offering called “Merge”, where she suggested upgrading humans so their learnings could be connected to the Cloud. Naturally, as a Cloud-native business, we took the hint: Cyborg could be SciborgTM, representing the concept of a next-generation type of scientific technologist. Not a professor, not a pharma employee, not a SaaS computer architect, but somebody special … new. And someone who could "bridge the gap between science, data, and technology to discover new ways of utilizing the number one asset of global biopharmas: scientific data."

Who are Tetra’s Sciborgs?

How do you find these people?” - Large Biopharma customer champion, remarking on TetraScience’s unique ability to attract people who exist at the nexus of our Sciborg skillsets

Sciborgs: The perfect blend of scientific domain expertise, technical architecture, data, and customer outcomes


Over the past three years, TetraScience has uniquely built a foundational employment brand that few can match. Thanks to extensive market research, internal training programs and experiential “on-the-job” learning in specific customer accounts on demanding scientific use case challenges, our Sciborgs match a specific phenotype despite their diverse backgrounds:

  • Sciborgs have personally felt the pain of inefficiency in laboratory, manufacturing, and “dry-lab” workflows. They commiserate, empathize, and synthesize learnings with customers to improve and standardize scientific workflows across the industry.
  • Sciborgs show great impatience with the current state: They saw change coming, the need for it, and embraced it. They plumbed up automation cascades using open-source Python libraries in graduate school. They developed lightweight dashboards for their company division. They used R to show statistical plots to support their decisions. They played with image feature recognition in their spare time. These “purple squirrels” didn’t wait to be “told” to learn - they embraced the future state.
  • Sciborgs live at interfaces: Science, data and technology. They are neuroscientists who love Python scripting. Chemists who revel in automation of continuous manufacturing processes. Antibody and immunology researchers who utilize R and Tableau to tell stories about their data.  Data scientists modeling results from offline CMC experiments to implement real-time release testing.


The behaviors above speak to will, but what about skill? Tetra Sciborgs aren’t just any data advisors!

  • Nearly all have Ph.D.s in a relevant scientific domain, or MS degrees in Engineering or Computer Science.
  • All have background knowledge in technical system architecture at the enterprise level.
  • They are intellectually nimble - they can quickly switch contexts to different phases of drug manufacture, integration modalities, or learn the nuances of a given scientific use case.
  • Finally, in case you missed it, above - they’ve lived in the lab in a prior life, where extraction of data from proprietary systems, processing, and preparation was still an ad hoc effort not covered by any solution currently on the market.

Sciborgs sound great, but what about AI/ML?

Artificial Intelligence and Machine Learning (AI/ML) are potentially transformational techniques underpinning self-operating “black-box” automated workflows for human problem solving like image recognition or the identification of potential lead drug candidates. While productized data integrations are upstream preconditions, the missing and essential piece is the Tetra Scientific Data and AI Cloud TM, the world’s only scientific data and AI cloud designed from the ground up, technically and commercially, to enable Scientific AI. Purpose-built for science and AI, open and vendor-agnostic, collaborative, and AI-native, it provides the biopharma industry with compliant, purpose-engineered, liquid, and actionable scientific data – aka Tetra Data. Think about Sciborgs like the “drivers” of this change, using the Scientific Data and AI Cloud as their toolkit. This enables these individuals to go “up the stack” from bottom-shelf data ingest and integration into the realm of use case optimization and data science ROI.


You just learned more in 30 minutes from my team than I’ve learned working with them over a year.”  - Large Biotech Customer

Biopharma customers have long suffered from inefficient legacy software with slow upgrade cycles. Their scientists (still) work off paper copies of data, don’t have access to dashboards, and their data is not yet FAIR.[4] They are often unable to imagine a different way of working, despite being overworked and understaffed for most research, development, and manufacturing projects. While labs are clearly staffed with experts, sophisticated in many areas of scientific technology, typically they have been cloud, data science, and data engineering laggards.

A number of systemic (industry) and structural (lab-level)challenges have slowed down the development of modern cloud, data science, and data engineering strategies in the lab. To wit, the very Scientific IT and Informatics teams who previously built apps and architecture in the “on-prem” world are now charged with facilitating the technological and cultural “digital transformation” to Cloud and AI.

Unfortunately, Scientific IT coders and managers often lack scientific frames of reference to inform the scientific data journey and determine how a Cloud tech stack can optimize it. They need help — from Tetra Sciborgs.

TetraScience Sciborgs are your trusted biopharma digital guides

Multiple customers have conveyed their eagerness to exploit the capabilities of the Tetra Scientific Data and AI Cloud, yet quickly conclude that they lack the integrated cloud, data engineering, and scientific data fluency and expertise to do this on their own, and actively seek our guidance and expanded partnership support.

Of course, we recognize that these skills exist on a spectrum- there are folks who are stronger on the tech and architecture, and others stronger on the customer pain points and scientific workflows. Thus, we have deployed a “two-in-the-box” approach to this problem.

Our scientific business analysts (SBAs) – classically-trained scientists who understand the Tetra Scientific Data and AI Cloud technology and its impact on scientific data – carry the metaphorical water in the beginning of a customer engagement. They understand customer pain points (having been scientists in biopharma themselves), map these to a genericized, industry-informed scientific use case, and suggest potential ways to move these to “To-Be” architectures. SBAs collaborate side-by-side with our customers’ scientists to identify new use cases and Scientific AI opportunities, which can uniquely benefit from the Tetra Scientific Data and AI Cloud to accelerate and improve scientific outcomes.

Their counterparts – scientific data architects (SDAs) – are cloud-native technology experts. They match customer requirements with appropriate solutions, and decipher how these will integrate and interact with the current software environment. They also identify the appropriate use cases for initial deployment and ensure smooth integration with a biopharma customer’s internal scientific IT landscape.

While our customers have talented data scientists on staff, they’re increasingly seeking to augment in these key areas, and deem our SBA sand SDAs invaluable extensions of their team given their unparalleled Sciborg superpowers – i.e., scientific, cloud, and data science expertise. SDAs and SBAs are embedded in our customers’ and partners’ day-to-day challenges, bringing life and context to their scientific use cases. This is true in high-throughput screening in research, through cell profiling, protein purification, and stability testing in development, to batch release testing in manufacturing of your biologic or small molecule therapeutics.

Clearly, our customers possess scientific expertise. They are NOT experts in the Tetra Scientific Data and AI Cloud. Our SBAs and SDAs bridge this knowledge and skills gap. They bring the Tetra Scientific Data and AI Cloud to life and relentlessly seek out new use cases and ways to optimize, accelerate, and improve scientific outcomes through its application.

Learn from our Sciborgs

To learn more about the impact of Sciborgs on end-to-end scientific workflows and the usage of Scientific AI in your labs, you can visit our website or check out our case studies. We also look forward to hearing from you directly: sciborgs@tetrascience.com


[1] Jorge Conde and Jay Rughani, Doing More with Moore: Biotech's Tech Moment | Andreessen Horowitz: https://a16z.com/2023/02/14/doing-more-with-moore/

[2] Jobs interview, 1981: https://www.youtube.com/watch?v=L40B08nWoMk

[3] FDSE Day in the Life: https://blog.palantir.com/a-day-in-the-life-of-a-palantir-forward-deployed-software-engineer-45ef2de257b1

[4] Wilkinson, M.D. et. al., 2016: https://www.nature.com/articles/sdata201618