Data Analyst (639392)

Salary range: £36,024 - £44,263

FTE: 1 

Term: Open-ended

Closing Date: 20 October 2024

Location: NMIS, Renfrew

The University of Strathclyde is a leading international technological institution and has a long history of working with industry to deliver strong business growth from access to research and innovation expertise. The latest major initiative continuing to deliver on this track record is through the National Manufacturing Institute Scotland.

As a magnet for innovation in advanced manufacturing, the National Manufacturing Institute Scotland (NMIS) group of specialist R&D centres (including the Digital Factory), supports manufacturing, engineering, and associated tech businesses of all sizes, to thrive domestically and internationally through accelerating productivity, embracing new digital technologies and achieving net-zero targets.

We turn smart ideas into reality and deliver ground-breaking research.

Coming from diverse backgrounds and disciplines, our passionate team works alongside industry, academia, and the public sector to solve problems, train the workforce of the future, and generate the creative ideas that will transform manufacturing.

The National Manufacturing Institute Scotland (NMIS) is the future of manufacturing at the heart of the Advanced Manufacturing Innovation District Scotland. It is where industry, academia, and the public sector work together on ground-breaking manufacturing research to transform productivity levels, make companies more competitive and boost the skills of our current and future workforce.

Empowering autonomous decision-making within manufacturing enterprises, whether large corporations or small and medium-sized enterprises (SMEs), stands as a cornerstone of digital transformation. Central to this endeavour are decision support systems driven by algorithms rooted in statistical analysis, machine learning, and artificial intelligence. Crafting these algorithms necessitates a profound exchange of knowledge between manufacturing domain experts and iterative refinement through building, training, and validating data-driven models.

A solid understanding of machine learning algorithms is essential. Additionally, you will be expected to transform raw data into compelling visual narratives that support decision-making in the manufacturing industry. Your role will involve developing innovative visualization solutions to present manufacturing data in a clear and engaging manner. Collaborating closely with data scientists, engineers, and UX designers, you will turn raw data into visual insights that drive informed decision-making.

To tackle these challenges head-on, NMIS is actively seeking a proficient Data Scientist. This role will bridge NMIS, the University, and its industrial partners, facilitating robust knowledge exchange initiatives including collaborative research and development.

The ideal candidate will bring to the table research and/or technical expertise across several domains:

• Proficiency in statistical analysis, machine learning, and artificial intelligence methodologies as applied to industrial datasets.
• Competence in designing and managing diverse database structures (i.e. relational, non-relational)
• Expertise in data exploration, visualization, and the seamless integration of ground truth sources into training datasets.
• Craft dashboards and data visualisations that effectively communicate complex data insights to stakeholders.
• Experience in developing models on remote servers for subsequent deployment to edge devices or business intelligence systems.
• Collaboration prowess, adept at working within interdisciplinary teams comprising data scientists, engineers, and other technical specialists.

To be considered for this role, you will be educated to a minimum of PhD level in in Computer Science, Engineering, or Mathematics or be educated to a minimum of 2:1 Honors degree in addition to significant relevant experience within a relevant industrial environment.

For informal enquiries, please contact Sarini Jayasinghe, Data Analystics Theme Lead, jayasinghe.jayasinghe@strath.ac.uk