Bioinformatics Scientist

On a bioinformatics scientist apprenticeship course, you’ll apply computational, data analysis and data mining techniques to a range of problems in the life sciences.

A bioinformatics scientist, a bioinformatician, uses computational, data analytical, and data mining methods to tackle life science problems such as drug discovery and development. Roles demand scientists who understand the life sciences and can deal computationally with diverse and massive data generated by numerous life science operations.

In general, you will explore, develop, and apply computational tools and approaches for expanding the use of life science data and obtain, store, organise, archive, analyse, and visualise this data. Allowing you to contribute to the development and use of data-analytical and theoretical methods, mathematical modelling, and computer simulation techniques for study.

As a bioinformatician, you will work as part of a collaborative group or team of scientists that comprise life scientists, statisticians, and specialists in computer infrastructure. 

What you’ll learn

On a bioinformatics scientist apprenticeship course, you’ll learn to:

• Collaborate with colleagues from many disciplines to design life-science experiments that will provide data suitable for subsequent bioinformatics analysis.
• Recognise and assess the format, breadth, and restrictions of distinct biological data sets.
• Define the information that must be collected for certain data kinds and analysis methodologies.
• Develop and implement appropriate data storage formats and database architecture.
• Choose the appropriate computational infrastructure and database solutions, considering both local and external/cloud resources.
• Use proper metadata, ontologies, and/or restricted vocabularies to curate biological data.
• Use relevant programming languages and/or workflow tools to automate data processing and curation operations.
• Maintain a working knowledge of a range of publicly available biological data sources.
• Prepare data for submission to appropriate public bioinformatics data repositories as required, keeping the intellectual property and/or ethical and legal considerations in mind.
• Determine the best bioinformatics analysis technique, including statistical test selection, while keeping the study topic and experimental design constraints in mind.
• Determine and establish the computing infrastructure required to analyse such biological data.
• Use a mix of modern approaches, skills, and technology (including computer languages) to practise computational biology – and
• Contribute to (and, if necessary, lead) research to create innovative methodologies.
• Create and test analytical pipelines, or develop and test new algorithms for biological data processing as required.
• Document all data processing, analysis, and new method implementation following accepted scientific practices and industry standards for regulatory processes and intellectual property.
• Interpret bioinformatics analysis results in the context of the experimental design and, if necessary, in a wider biological context by incorporating other (often publicly available) data.
• Obtain data sets from private and/or public sources while keeping any legal, privacy, or ethical consequences of data use in mind.
• Analyse and visualise biological data using appropriate programming approaches, statistical and other quantitative and data integration tools.
• Communicate bioinformatics analyses and results to a wide range of audiences, including interdisciplinary scientific colleagues, non-scientific management members, external partners and stakeholders, grant/funding organisations, and the general public as required.
• Oversee and train colleagues and peers to study bioinformatics about their specific life science issue knowledge.

Entry requirements

You’ll usually need:

• Background in a life sciences subject or informatics/ computer science.
• Apprentices without level 2 English and maths will need to achieve this level before taking the end-point assessment.

Assessment methods

The End Point Assessment consists of two distinct assessment methods: 

• A synoptic report, followed by a presentation with discussion (Q and A)
• A viva-style professional conversation supported by a vocational competence evaluation log

Duration, level, subjects and potential salary upon completion

Apprenticeship standard

More information about the Level 7 Bioinformatics Scientist Apprenticeship standard can be found here.

Apprenticeship end point assessment

For more information about the End Point Assessment Process, please read the Institute of Apprenticeships’ information page.