Our people share our purpose and vision for the future; to deliver a healthy and sustainable world in which we all live and work through our exceptional workplace services and consultancy, communications and scientific research.

We have been at the forefront of occupational health research since 1969, to understand and remove a range of health risks. These include noise, chemicals, asbestos, heat, dust or vibration and emerging risks from new technologies such as nanomaterials. We are uniquely placed to practically apply our research findings and deliver services to businesses that help manage hazards in the workplace.

If you would like to join our team click to view our vacancies below or send your C.V. and covering letter to [email protected]

Apprentice Data Scientist

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• Full-time (37.5 hours)
• PENICUIK
• SCQF Level 10
• Data Science

Main Duties

As a Graduate Apprentice in Data Science at IOM, you will support the organisation and its clients by providing data services and research which will help to drive informed decision-making, enhance stakeholder engagement, and strengthen commercial outcomes. Working across teams, you will Contribute to and support a wide range of research and consultancy projects, including data curation, cleaning, processing, management, analysis, visualisation, and interpretation. This role offers a unique opportunity to develop practical data science skills while contributing to real-world impact in a purpose-led, B2B environment, with a strong emphasis on collaboration, innovation, and continuous learning.

Work activities:

· Contribute to and support a wide range of research and consultancy projects, including data curation, cleaning, processing, management, analysis, visualisation, and interpretation

· Link datasets from multiple sources (e.g. sensors, surveys, administrative records) · Support the development of reproducible data pipelines

· Apply quality assurance procedures and identify inconsistencies, biases, and missingness in complex datasets

· Support quantitative, statistical, and geospatial analysis, including the use of AI and machine learning, under supervision

· Engage with real-world data challenges such as small sample sizes, noisy sensor data, and heterogeneous data sources

· Implementation of data governance, confidentiality, and ethical considerations in handling health and workplace data

· Learn to interpret and present findings from a variety of human exposure data (including from sensors and surveys) using effective data visualisation methods

· Contribute to articles for peer-reviewed publication and client-facing reports

· Develop and maintain scripts, tools, or software modules to automate repetitive data processing tasks

· Assist in integrating and managing large-scale datasets using appropriate database or cloud solutions

IOM is an organisation with a great history and unlimited potential for the future.

Many of our scientific staff, past and present, are internationally recognised as leading thinkers in their subject areas. Our senior scientists have acquired a broad grasp of several sciences as well as their speciality, and this collaboration has been behind many of the fundamental advances made over our history.