Arctoris is seeking a Senior Scientific Data Scientist / Pipeline Engineer to design, implement, and maintain the analytical pipelines that power our data-driven drug discovery platform.

This role is ideal for a scientist-turned-coder or data scientist with strong Python skills, statistical modelling expertise, and experience with bioassay data - paired with solid software engineering discipline.  

If you thrive at the intersection of biology, data, and scientific automation, enjoy partnering with wet-lab teams, and want to build robust tools that accelerate therapeutic discovery, this is the ideal opportunity.

Scientific Analysis & Pipeline Development

• Build, maintain, and extend Python-based analytical pipelines for diverse bioassay datasets (biochemical, biophysics, and cell-based assays).
  
  

• Implement robust statistical and modelling workflows, including:
  
  

  • dose–response modelling and curve fitting (e.g., 4PL, mechanistic models)
    
    

  • QC and normalisation frameworks
    
    

  • plate-level statistics and data validation
    
    

• Translate scientist workflows into reproducible, automated analysis pipelines.
  
  

Data Engineering & Workflow Structuring

• Design structured ETL/ELT processes for experimental data ingestion, curation, and transformation.
  
  

• Develop clean, maintainable, well-tested Python codebases using solid software engineering principles.
  
  

• Use lightweight workflow tools (e.g., Snakemake, Luigi, or similar) to organise multi-step scientific analyses.
  
  

Scientific Collaboration & Communication

• Partner closely with wet-lab scientists to:
  
  

  • understand assay logic and experimental design
    
    

  • identify improvements to data structures and analysis readiness
    
    

  • streamline file preparation and data handover
    
    

• Provide input on experimental interpretation where it helps clarify analytical or QC decisions.
  
  

Infrastructure & Tooling (Nice-to-Have)

• Support backend integration with cloud storage or databases (AWS S3, PostgreSQL, etc.).
  
  

• Contribute to internal scientific tooling, dashboards, or lightweight GUIs (optional).
  
  

• Assist with continuing evolution of scientific data architecture.

Scientific & Analytical

• Strong experience analysing plate-based bioassay data (384- or 1536-well formats).
  
  

• Proficiency in Python for scientific computing, including NumPy, Pandas, SciPy, curve fitting libraries such as lmfit, and visualisation libraries such as Matplotlib/Seaborn. Experience with scikit-learn for exploratory analysis (e.g., PCA, clustering), data validation tools (e.g., Pandera), and scientific libraries such as Biopython or RDKit is highly beneficial.
  
  

• Solid understanding of statistical modelling, curve fitting, and QC in experimental biology.
  
  

• Ability to work across multiple scientific domains and adapt to varied assay formats.
  
  

Software Engineering

• Proficient in:
  
  

  • modular design and code organisation
    
    

  • testing (PyTest/Unittest)
    
    

  • version control (Git)
    
    

  • clear documentation and reproducibility
    
    

• Experience developing end-to-end data or analysis pipelines.
  
  

Collaboration & Communication

• Able to work fluidly across scientific teams and translate scientific needs into computational workflows.
  
  

• Clear and structured communicator; capable of articulating analytical choices and modelling approaches.
  
  

• Strong problem-solving mindset.

Desirable Skills

• Experience with lightweight workflow managers (e.g., Snakemake, Luigi).
  
  

• Familiarity with AWS or cloud storage systems.
  
  

• Database experience (PostgreSQL/MySQL) and data modelling.
  
  

• Background in biophysics, cell biology, enzymology, or other relevant scientific domains.
  
  

• Exposure to containerisation (Docker) or CI/CD (GitHub Actions). 

• Ability to develop simple user-facing interfaces (e.g., Streamlit).