Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Scientific Data Architect - United Kingdom

TetraScience
Macclesfield
5 days ago
Create job alert
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: Latest News and Announcements | TetraScience Newsroom:


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 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 7+ years / Masters with 10+ 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

  • PhD with 7+ years or Masters with 10+ 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 experience in defining, designing, prototyping, and implementing productized AI/ML‑driven use cases in cloud environments
  • Strong ability to collaborate with cross‑functional teams, including product managers, software engineers, and scientific stakeholders
  • Capacity to perform extensive exploratory data analysis and workflow optimization to support scientific outcomes
  • Excellent communication and storytelling abilities to engage diverse audiences including scientists and executive stakeholders
  • Track record of advising scientists in a consulting capacity to further research, development, and quality testing outcomes

What You Will Do

  • 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

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.


#J-18808-Ljbffr

Related Jobs

View all jobs

Scientific Data Architect - United Kingdom

Scientific Data Architect - United Kingdom

Scientific Data Architect - United Kingdom

Data Engineer

Senior Scientific Data Engineer

Tech Lead, Scientific Data Engineer in Cambridge

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.