Job description
We are seeking a PhD graduate (or close to completion) who will be
responsible for the
development and integration of a bespoke and compatible forecasting
solution that can
capture the high-level of demand-uncertainty Priory Direct is facing. PBG
is a sustainable
packaging retailer based in the southeast of the UK. The company focuses on
the provision
of low impact and charity aligned stock packaging products and custom
packaging products
that drive greater efficiency and brand awareness while minimising
environmental and climate
impact. PBG currently supply over 21,000 businesses in the UK and
predicting customer
demand and planning of stock is at the core of their business and value
proposition. As such
the Associate will be an integral part of the Procurement Team to create a
new bespoke and
market-leading approach using cutting edge knowledge in the fields of data
analytics and datadriven forecasting to solve the challenge of demand
forecasting in a highly volatile market.
The Associate will be based at the PBG’s premises in Kent and will work
closely with the
support and guidance of Dr Ramin Raeesi and Dr Zhen Zhu from the Kent
Business School
(KBS), University of Kent. At the end of the fixed term contract, and upon
successful
completion of the project, there is a possibility that the role could
become a permanent fulltime position.
Key Accountabilities / Primary Responsibilities
• Collect and process the required data and prepare them for analysis.
• Development of the required data-driven forecasting model and carry out
further
model training and validation activities required to ensure model accuracy,
reliability,
and suitability for implementation.
• Reporting on each phase of testing to stakeholders and scientific
communities.
• Customer portfolio analysis and segmentation, development of customer
segment
adaptive replicas, implementation within legacy systems, automating data
acquisition
and analysis events and managing user acceptance and adoption within the
team.
• Writing of scientific papers associated with the work for publication
in peer reviewed
scientific journals.
Key Duties
• Read academic literature and undertake training to update own knowledge
in the fields
of predictive modelling.
• Developing solutions to deliver data analytics-based insights.
• Ensuring performance and quality of data analytics methods.
• Visiting PBG offices and attending necessary meetings/training.
• Collaborating with PBG colleagues and academic supervisors to deliver
data-driven
insights.
• Integration of solutions into the PBG’s Procurement systems.
• Producing training material towards the end of the project.
Such other duties, commensurate with the grading of the post that may be
assigned by the
academic advisor or their nominee.
Project description
To improve efficiency and service levels within a sustainable packaging provision context to the fast-growing ecommerce sector. The project is expected to lead to an increase in market share and improvements in stock availability and demand forecasting through enhanced data handling and supply chain analytics.
About the business

Priory Business Group Plc (Priory Direct)


Sustainable packaging/e-commerce
Data Science/Business Analytics
MSc or ideally a PhD in operational research (OR), management science, business analytics, or similar
6 September 2023
1 October 2023
20 November 2023
KBS-171-23
We are seeking a PhD graduate (or close to completion) who will be responsible for the
development and integration of a bespoke and compatible forecasting solution that can
capture the high-level of demand-uncertainty Priory Direct is facing. PBG is a sustainable
packaging retailer based in the southeast of the UK. The company focuses on the provision
of low impact and charity aligned stock packaging products and custom packaging products
that drive greater efficiency and brand awareness while minimising environmental and climate
impact. PBG currently supply over 21,000 businesses in the UK and predicting customer
demand and planning of stock is at the core of their business and value proposition. As such
the Associate will be an integral part of the Procurement Team to create a new bespoke and
market-leading approach using cutting edge knowledge in the fields of data analytics and datadriven forecasting to solve the challenge of demand forecasting in a highly volatile market.
The Associate will be based at the PBG’s premises in Kent and will work closely with the
support and guidance of Dr Ramin Raeesi and Dr Zhen Zhu from the Kent Business School
(KBS), University of Kent. At the end of the fixed term contract, and upon successful
completion of the project, there is a possibility that the role could become a permanent fulltime position.
Key Accountabilities / Primary Responsibilities
• Collect and process the required data and prepare them for analysis.
• Development of the required data-driven forecasting model and carry out further
model training and validation activities required to ensure model accuracy, reliability,
and suitability for implementation.
• Reporting on each phase of testing to stakeholders and scientific communities.
• Customer portfolio analysis and segmentation, development of customer segment
adaptive replicas, implementation within legacy systems, automating data acquisition
and analysis events and managing user acceptance and adoption within the team.
• Writing of scientific papers associated with the work for publication in peer reviewed
scientific journals.
Key Duties
• Read academic literature and undertake training to update own knowledge in the fields
of predictive modelling.
• Developing solutions to deliver data analytics-based insights.
• Ensuring performance and quality of data analytics methods.
• Visiting PBG offices and attending necessary meetings/training.
• Collaborating with PBG colleagues and academic supervisors to deliver data-driven
insights.
• Integration of solutions into the PBG’s Procurement systems.
• Producing training material towards the end of the project.
Such other duties, commensurate with the grading of the post that may be assigned by the
academic advisor or their nominee.