About the role
Portal is seeking a motivated Data Scientist to join our Bioinformatics & Machine Learning team. In this 1-year, fixed-term role, you will work directly with the wet lab, applying techniques from statistics and machine learning to invent new applications of Portal's single-molecule proteomics platform. The work spans the full range from exploratory data analyses to shipping production-hardened methods, and is embedded in a fast-moving, highly collaborative research environment at the scientific frontier of the field. This role is ideal for someone who is excited by novel methodological challenges, applies scientific rigour and first principles approach to their work, and is comfortable in rapidly evolving and often ambiguous problem spaces. Tasks and responsibilities
Qualifications
Portal is seeking a motivated Data Scientist to join our Bioinformatics & Machine Learning team. In this 1-year, fixed-term role, you will work directly with the wet lab, applying techniques from statistics and machine learning to invent new applications of Portal's single-molecule proteomics platform. The work spans the full range from exploratory data analyses to shipping production-hardened methods, and is embedded in a fast-moving, highly collaborative research environment at the scientific frontier of the field. This role is ideal for someone who is excited by novel methodological challenges, applies scientific rigour and first principles approach to their work, and is comfortable in rapidly evolving and often ambiguous problem spaces. Tasks and responsibilities
- Research, development, and shipping of analytical pipelines for nanopore-based proteomic data
- Design and implementation of statistical and ML approaches to novel biological data, iterating from exploratory analysis through to deployable pipeline components
- Robust evaluation and benchmarking of algorithmic approaches, with clear documentation of findings
- Liaise with experimental scientists and engineers to understand data characteristics, interpret results, and align on deliverables
- Present analytical results and methodological decisions to multidisciplinary audiences, including non-technical stakeholders and clients
- Contribute to a culture of scientific rigour, reproducibility, and code quality within the team and outwards to the entire company
Qualifications
- Master's degree or higher in Bioinformatics, Statistics, Machine Learning, or a related field
- Strong theoretical grounding in statistics and ML, with the ability to apply it in biological contexts
- Solid command of DS best practices: major Python DS/ML libraries, data science experimentation workflows, Git version control, and ability to read and reuse code from existing projects
- Demonstrated data engineering fundamentals, including handling messy data, applying robust cleaning and validation practices, and exercising good judgement as data complexity increases
- A genuine, considered view on the use of AI tools in day-to-day technical work, where they add value, and where they don't
- Ability to ship code from analysis notebooks to robust, reusable pipeline components
- Familiarity with experiment tracking tools and shared compute environments and/or cloud platforms
- Demonstrates strong scientific curiosity and a data hunter mindset; asks good clarifying questions and is comfortable navigating complex, ambiguous, and under-specified problems
- Ability to work effectively across experimental, engineering, and commercial functions, and explain complex technical work to non-specialist audiences
- Strong internal and external collaborator, with an outward looking, market-aware mindset and the ability to articulate technical value in terms of customer and business impact
- A theoretical background in molecular biology or protein biochemistry, including foundational understanding of proteomics-related concepts
- Hands-on experience developing data science pipelines for omics or other complex biological data, including familiarity with relevant data standards and formats
- Experience with unsupervised or semi-supervised learning, Bayesian modelling, or time-series data
- Experience with or interest in single-molecule or electrophysiology data
- Demonstrated ability to learn new biological domains, data modalities, or analytical techniques quickly and effectively
Vacancy posted 14 days ago
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