Research Assistant

Edinburgh Napier Business School is the Business School for empowerment, enterprise and employability for all. Offering a distinctive, inclusive and dynamic educational environment, it is one of the largest Business Schools in Scotland.

Research at the Business School has an applied focus, is policy and practice led, and is of both national and international relevance.

Currently, we are recruiting a Research Assistant to support a project that will integrate predictive analysis of historical sales and stock data into The Bicycle Association (BA)'s Market Data Service (MDS) to enhance cycling products supply chain optimisation.

The Role:

This role is an excellent opportunity for you as an early career academic, to provide valuable support to this unique, high-impact project that directly influences strategic stock holding decisions based on a comprehensive understanding of potential demand variability.

As Research Assistant, you will have the chance to use your experience in developing and implementing forecasting models (ideally with a focus on their application in inventory management or supply chain optimisation) to research, develop, and implement appropriate statistical and machine learning forecasting models to generate reliable demand forecasts for segments of bicycle products.

Your strong understanding of the forecasting process, statistical forecasting techniques, judgemental forecasting and ideally experience with machine learning algorithms for time series forecasting (e.g., Prophet, Ensemble methods, Joint Time Series Modelling, etc) will also serve you well as you partner with stakeholders to thoroughly understand available data, their forecasting needs, challenges in current data quality and stock control processes, and desired functionalities of the tool.

Furthermore, your experience in designing and developing user-facing tools or integrating analytical models into existing systems (e.g., using Python Software Development Kits) will allow you to develop mechanisms to translate low, medium, and high forecasted demand scenarios into communicable, actionable insights for determining appropriate stock holding levels under different potential future outcomes.

The post will not only give you the prospect for professional development and growth (with potential to join other research projects), it will also allow you to develop and deepen your professional networks in academia, the cycling industry and related domains.

What we will need from you:

• Masters in a quantitative field such as Analytics, Operational Research, Computer Science, Data Science, Statistics, Mathematics, or a related discipline.
• Experience in developing and implementing forecasting models, ideally with a focus on their application in inventory management or supply chain optimisation.
• Strong understanding of the forecasting process, statistical forecasting techniques
• Proficiency in data manipulation and analysis using languages such as Python (with libraries like Pandas, NumPy, Scikit-learn, Prophet) or R.
• Strong analytical and problem-solving skills with the ability to interpret complex data

For a full role description and comprehensive list of duties, please click here.

Benefits we offer:

46 days annual leave (includes bank holidays) – pro rata, generous pension scheme with Employer contribution of 17.6%, hybrid/flexible working and professional development opportunities. Further information can be viewed here.

Contract: Fixed term – 7 months

Hours: Full time – 35 hrs/wk

Additional information:

Interviews: Week commencing 2^th June 2025

The University is committed to inclusion, demonstrated through our work in respect of our diversity awards and accreditations and hold Disability Confident, Carer Positive and Stonewall Scotland Diversity Champion status. More details can be found here. We are a flexible Employer.

Advert information