Scientific Data Scientist

Arctoris
Oxford
3 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist, United Kingdom - BCG X

Data Scientist, United Kingdom - BCG X

Data Scientist, United Kingdom - BCG X

Data Scientist, United Kingdom - BCG X

Data Scientist, United Kingdom - BCG X

Join to apply for the Scientific Data Scientist role at Arctoris


Arctoris is seeking a Senior Scientific Software Engineer / Data Scientist to design and implement analytical pipelines and peripheral software architectures and tools that power data-driven drug discovery. This role is ideal for a data scientist and/or developer with strong foundations in scientific data engineering, bioassay data analysis, and full-stack software design, who thrives at the intersection of computational biology and software engineering.


Main Responsibilities

  • Architect, develop, and maintain analytical and visualization pipelines for diverse bioassay data and project requirements.
  • Design and build pragmatic scalable scientific tools and data processing frameworks to support Arctoris’ experimental platforms.
  • Implement interactive GUIs and web applications for experimental data management, decision support, and visualisation.
  • Develop robust, maintainable codebases and contribute to best practices in software design, CI/CD, testing, and documentation.
  • Collaborate with data scientists, biologists and drug discovery experts to provide meaningful data products and insights.
  • Optimise integration between lab automation systems, databases, and analytical pipelines.
  • Contribute to data infrastructure evolution, including APIs, orchestration layers, and cloud architectures.

Qualifications and Experience

You have a track record of building performant software for data-rich scientific environments. This role bridges analytical insight with engineering rigor, designing data systems that accelerate hypothesis generation and validation in modern drug discovery. With this in mind, we are seeking someone who is motivated by impact, precision, and the application of software engineering to real-world biology.


Essential Skills

  • Advanced proficiency in Python for scientific and analytical computing, including: Pandas, NumPy, Scikit-learn, Jupyter, RDKit, Biopython
  • Experience building data ingestion, curation, and transformation pipelines.
  • Expertise in software engineering principles: modular design, testing (PyTest/Unittest), and version control (Git).
  • Strong command of data modeling and database design (PostgreSQL, MySQL, MongoDB, or equivalent).
  • Proven experience in web application frameworks (FastAPI, Django, or Flask) and modern front-end technologies (React or Vue).
  • Familiarity with scientific workflows and bioassay data structures common in drug discovery.

Desirable Skills

  • Knowledge of data orchestration frameworks (Prefect, Airflow, Jenkins).
  • Experience deploying systems on AWS, Azure, or GCP.
  • Proficiency in containerization and CI/CD (Docker, GitHub Actions, Bitbucket Pipelines).
  • Understanding of DevOps practices, cloud infrastructure automation (Terraform, Ansible), and scalable API architectures.

Exposure to lab informatics, LIMS, or automation system integration.


#J-18808-Ljbffr

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.

Careers

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

Jobs

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

Education

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.