Scientific Data Analyst

2 weeks ago

Oxford, United Kingdom Oxford Nanopore Technologies Full time

Oxford Nanopore Technologies is headquartered at the Oxford Science Park outside Oxford, UK, with satellite offices and commercial presence in many global locations across the US, APAC and Europe.

Oxford Nanopore's sequencing platform is the only technology that offers real-time analysis (for rapid insights), in fully scalable formats from pocket to population scale, that can analyse native DNA or RNA and sequence any length of fragment to achieveshort to ultra-long read lengths.

Our goal is to enable the analysis of any living thing, by anyone, anywhere. We offer real-time Nanopore-based DNA/RNA sequencing technology: accessible, easy to use and fully scalable for any requirement.

We are looking for a highly motivated individual to join our Machine Learning Research group as a Scientific Data Analyst / Algorithms Researcher. This exciting and challenging role involves exploratory analysis of nanopore sequencing data and developmentof new data analysis methods

The Details...
As a member of the Machine Learning Research group you will:

- Perform exploratory analysis research projects to identify leads to improving platform accuracy.
- Prototype and implement new algorithms and machine learning methods to follow up leads.
- Consolidate analyses into reproducible, standardised, and scalable tools when they become routine.

Examples of projects you might work on include analysing sequencing data to inform research priorities, developing consensus and variant callers, or crafting nanopore-tailored bioinformatics tools.

The ability to interact with scientists to propose a hypothesis, help plan an experiment, provide custom analysis to examine a dataset, and refine the hypothesis, is crucial along with a strong understanding of scientific methods and excellent interpersonalskills.

What We're Looking For...
Knowledge, skills and abilities in the following areas are essential:

- Excellent working knowledge of applied statistics.
- Fundamentals of experimental design, training and validation of statistical models.
- Substantial experience of developing new methods to analyse data in bioinformatics/computational biology or the physical sciences.
- Strong programming skills (Python)
- Development of software containing a substantial numerical component.
- Use of Linux/Unix environment and associated development toolchain.
- Working in a time-critical environment with responsibility for reporting progress.
- Ability to carefully document your work.

Ideally, you will also have knowledge or familiarity in the following areas:

- An appreciation of techniques fundamental to bioinformatics including sequencing data analysis methods.
- Prototyping new algorithms, implementing methods directly from research papers.
- Exposure to high-level machine learning frameworks (e.g. keras).
- Familiarity with C.

You should be able to quickly understand complex scientific problems and work with a high degree of independence.

LI-JC1

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