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About the Role
We are a stealth startup in AI and bio, building infrastructure for data generation. This role will own the analysis of assay data and the associated data and ML infrastructure, from data capture to result delivery. You will work closely with scientists and the software team, building on their platform and owning the quality of the analysis.
Responsibilities
- Own the analysis of assay data and the data and ML infrastructure it runs on.
- Build analysis and ML pipelines that turn captured assay and sequence data into consistent, versioned datasets.
- Perform data validation before analysis runs.
- Conduct sequence-level bioinformatics, including computing protein sequence properties and checking constructs.
- Perform biostatistics for experimental data, including experimental design, replicates, controls, error propagation, outlier handling, and assay acceptance criteria.
- Perform curve fitting, including dose-response with normalization and fits to various traces, with confidence measures and automated QC.
- Detect plate, lot, and batch effects across datasets.
- Implement control charting of reference standards.
- Develop data visualization for delivered results, showing uncertainty, QC, and batch structure.
Requirements
- Experience building and running production data and ML pipelines, ideally on scientific or lab data.
- Experience with data modelling, validation, and reproducible, versioned workflows.
- Experience handling biological data specifics: plate layouts, sample and reagent lot lineage, protein and DNA construct sequences.
- Experience with biostatistics for experimental data, including experimental design, replicates, controls, curve fitting, and separating batch effects.
- Experience with statistical process control and anomaly detection.
- Experience with data visualization for non-technical audiences.
- Fluency in Python and its scientific stack (pandas, NumPy, SciPy).
- Experience working with coding agents.
- Experience with SQL and relational schema design.
- Experience turning scientists' questions into requirements and documenting assumptions.
- Experience with messy experimental data and building reliable systems.
- Willingness to perform careful, repeated work on real instrument output.
- Ability to write code that others can run and care about how results are presented.
- Willingness to stretch beyond the described role, potentially into the lab or broader discussions.
- Ability to pick up new tasks and responsibilities as they arise.
Skills
- Data Engineering
- Machine Learning
- Biostatistics
- Bioinformatics
- Data Validation
- Data Visualization
- Python
- Pandas
- NumPy
- SciPy
- SQL
- Relational Schema Design
- Experimental Design
- Curve Fitting
- Statistical Process Control
- Anomaly Detection
- Docker
- Kubernetes
- AWS
- LIMS
- Electronic Lab Notebooks
- Protein Language Models
- Structure Prediction
Location
- King's Cross, London
Work Type
- Hybrid
- Regular time in the lab
Experience Level
- Early career to many years of experience
- Depth and judgement on experimental data
Benefits
- 30 days of annual leave plus public holidays
- Pension with a 10% employer contribution
- Bupa private health cover
About the Company
- AI for biology startup building infrastructure for data generation.
- Focused on producing data with quality and traceability from day one.
- Currently in stealth mode and heads-down on execution.
Equal Opportunity
- Equal opportunity employer.
- Hiring decisions based on merit, scope-fit, and expected working relationship.
- Applications welcome from candidates of any background.