Senior Scientific Data Scientist

Senior Scientific Data Scientist / Pipeline Engineer
Remote / Hybrid Oxford
6 Month Contract
Outside IR35
£400 to £550 per day

Overview

A technology-enabled drug discovery company is looking for a Senior Scientific Data Scientist to design, build and maintain the analytical pipelines that power its data-driven discovery platform.

The business combines automated wet-lab experimentation, proprietary analytical pipelines and deep scientific expertise to generate high-quality, reproducible biological datasets at scale. This data supports AI-driven discovery work across areas including oncology, immunology and neurodegeneration.

This role would suit a scientist-turned-coder or scientific data scientist with strong Python skills, statistical modelling experience and a background working with bioassay or experimental biology data.

The Role

You will build and improve Python-based analytical pipelines for diverse bioassay datasets, including biochemical, biophysical and cell-based assays.

You will work closely with wet-lab scientists to understand assay logic, experimental design and data handover processes, then translate those workflows into automated, reproducible analysis pipelines.

The work will involve dose-response modelling, curve fitting, QC, normalisation, plate-level statistics and data validation, helping scientists generate clearer, faster and more reliable insight from complex experimental data.

Key Responsibilities

• Build, maintain and extend Python-based analytical pipelines for bioassay datasets
• Develop statistical and modelling workflows, including dose-response modelling, 4PL curve fitting and mechanistic models
• Build QC, normalisation and data validation frameworks for experimental biology data
• Support plate-level statistics and analysis of high-throughput assay outputs
• Translate wet-lab scientific workflows into reproducible automated pipelines
• Improve data structures, file preparation and analysis readiness
• Partner with wet-lab scientists to understand assay design and experimental logic
• Clearly communicate analytical choices, QC decisions and modelling approaches
• Build robust, documented and reusable scientific software

Essential Skills

• Strong Python experience for scientific computing
• Experience with Pandas, NumPy, SciPy and visualisation libraries such as Matplotlib or Seaborn
• Experience analysing bioassay, assay or experimental biology data
• Understanding of dose-response modelling, curve fitting, 4PL, EC50 / IC50 or similar
• Experience with QC, normalisation, data validation or plate-level statistics
• Experience building reproducible analytical pipelines or automated workflows
• Good software engineering habits, including Git, documentation and reproducibility
• Ability to work closely with wet-lab scientists and translate scientific requirements into computational workflows

Nice to Have

• Experience with 384-well or 1536-well plate-based assay data
• lmfit, Pandera, scikit-learn, Biopython or RDKit
• Background in drug discovery, biotech, pharma or CRO environments
• Experience across biophysics, cell biology, enzymology, oncology or immunology
• Experience with high-throughput screening, assay validation or automated lab platforms

The Candidate

The ideal candidate will be scientifically fluent and technically hands-on. You may come from a scientific data science, computational biology, bioinformatics, research software engineering or scientific software background.

You should be comfortable working with complex experimental data, applying statistical rigour and building tools that improve speed, reproducibility and scientific insight across a multidisciplinary team.

Desired Skills and Experience

Python, Pandas, NumPy, SciPy, Bioassay Analysis, Dose Response, Curve Fitting, 4PL, EC50, IC50, Scientific Computing, Data Science, Statistical Modelling, Quality Control, Data Validation, Drug Discovery, Biotechnology, Bioinformatics, Computational Biology, Git

Python-based scientific data analysis, bioassay data, dose-response modelling, 4PL curve fitting, EC50 / IC50 analysis, QC and normalisation workflows, plate-based assay data, reproducible analytical pipelines, scientific computing with Pandas / NumPy / SciPy, Git, documentation, experimental biology, drug discovery, biotech or pharma environments.