Computational Biologist

Epoch Biodesign

Greater London

Hybrid

GBP 60,000 - 90,000

Full time

14 days+

Get more replies from employers

Send a job-specific resume in minutes.

Benefits offered by this job

30 days paid holiday

EMI Share Options

9% employer pension contribution

Private medical insurance

Flexible working hours

Cycle to work scheme

Onsite gym

Cycle to work

Fruit & snacks

Onsite gym

Job summary

A cutting-edge biotechnology start-up in Greater London is seeking a Computational Biologist to develop and improve computational tools for enzyme design and screening. The ideal candidate will have a PhD in a relevant field and hands-on laboratory experience. Responsibilities include developing computational pipelines, analyzing high-throughput screening data, and collaborating with experimental teams. This entry-level position offers competitive benefits, including 30 days of paid holiday, flexible working hours, and generous pension contributions.

Qualifications

PhD or equivalent experience in biochemistry, molecular biology or related field.
Hands-on experience with laboratory techniques relevant to protein engineering.
Proven computational delivery using Python and scientific libraries.
Experience with protein structure analysis or design workflows.

Responsibilities

Develop and maintain computational pipelines for protein design.
Analyse high-throughput screening data to inform decision-making.
Prototype analyses and transition to robust production code.
Collaborate with experimental teams to clarify requirements and address variability.
Review literature and methods to support tool choice and approaches.
Maintain documentation and data frameworks for knowledge transfer and filings.

Skills

Protein/enzyme engineering

Python

High-throughput screening

Data analysis

Scientific libraries (NumPy, Pandas)

Plotly

PyMOL

Rosetta

Git

Linux

Reproducible pipelines

Protein engineering

Education

PhD in relevant field

Tools

PyMOL

Rosetta

Version control (git)

Linux/Unix

Nextflow

Job description

Join to apply for the Computational Biologist role at Epoch Biodesign

Epoch Biodesign is a well-funded, venture-backed start-up using biology to make every type of plastic recyclable - starting with nylon.

Using a unique combination of AI, synthetic biology and green chemistry, we are scaling enzymatic recycling in order to transform currently unrecyclable plastics and textiles into new, virgin-quality materials. Our technology yields substantial reductions in carbon emissions with disruptive unit economics, preventing waste from entering landfill or the environment, allowing us to solve this very urgent challenge.

With our pilot plant already processing nylon 6,6 waste at the multi-tonne level, we will imminently complete construction on our larger demo facility. This site will produce material destined for use in garments made by some of the world’s biggest fashion, sportswear and luxury brands, and also in components for some of the world’s largest car companies.

As a Computational Biologist at Epoch, your core function will be to: to support enzyme design and screening cycles by delivering practical computational protein design tools and analyses. The successful candidate will be able to translate well‑scoped questions into reproducible analyses and pipelines, and will be comfortable iterating rapidly as new data becomes available. Full domain coverage is not expected on day one; a strong foundation, delivery focus, and willingness to learn are essential.

Key Responsibilities

Develop, improve and maintain computational pipelines for both protein design (discovery) and analysis of subsequent high‑throughput screening data (optimisation)
Analyse high‑throughput screening/assay data and communicate conclusions in a form that directly informs experimental decision‑making, and delivers insights to guide future iterations of protein libraries
Rapidly prototype minimally viable analyses and tools, and subsequently transition successful solutions into robust, maintainable production code
Work closely with experimental stakeholders to clarify requirements, identify sources of assay variability/artifacts, and iterate on analyses based on laboratory feedback
Conduct targeted literature and method reviews to support tool selection and analytical approaches, ensuring relevance and currency
Maintain high‑standard documentation and data frameworks that facilitate internal knowledge transfer and, when required, can be used to support patent filings, grant applications and publications

Essential Qualifications and Experience

PhD (or equivalent research experience) in a relevant field with a strong experimental component (biochemistry, molecular biology, biotechnology, chemical biology, bioengineering, or related). We encourage applications from candidates who have recently completed their PhD and are looking to transition to an innovative and collaborative start‑up environment
Meaningful hands‑on laboratory experience relevant to protein/enzyme engineering (e.g., cloning/expression/purification and/or assay development/high‑throughput screening), including ability to interpret results within a design–build–test–learn workflow
Demonstrated computational delivery applied to experimental datasets, including strong working knowledge of Python and common scientific libraries (e.g., NumPy, Pandas, SciPy; visualisation with Matplotlib/Plotly or equivalent), and evidence of reproducible end‑to‑end analyses/pipelines (scripts, tools, or documented notebooks)
Hands‑on experience with common protein engineering and structural visualisation tools, including PyMOL and Rosetta, with demonstrated prior use in protein structure analysis and/or design workflows
Evidence of scientific output relevant to protein engineering or computational biology (e.g., publications, preprints, datasets, internal tools, or open‑source contributions)
Ability to work independently on well‑scoped tasks, including communicating risks early and seeking input when appropriate

Beneficial Qualifications and Experience

Experience with version control (git) and Linux/Unix environments
Working understanding of one or more of: bioinformatics/NGS data analysis, molecular modeling (docking/MD), computational chemistry (RDKit) or machine learning methods used in protein engineering
Experience working in interdisciplinary environments spanning computational and experimental teams
Exposure to cloud platforms (GCP/AWS) and/or workflow tooling (e.g., Snakemake/Nextflow) is beneficial

Benefits and perks