Dr. Christopher Dillon

Defined and built the data strategy, infrastructure, and team for a group of 18 businesses. Co-founded the Global Finance and Data Analytics Department — owning the full stack from data warehousing and ETL through to executive dashboards and advanced analytics.

Key Impact & Responsibilities:

• Business Impact: Increased OTIF by 25%, identified millions in excess & obsolete inventory, detected ~1% of revenue in out-of-stock sales loss.
• Team Leadership: Built and lead a team of PhD-level data scientists and engineers, supporting senior leadership across the group.
• Full-Stack Data Infrastructure: Designed and delivered centralised global reporting and extensive Streamlit dashboards (with Entra SSO), replacing fragmented legacy systems with a universal architecture — from 50,000ft strategic view to microscopic operational detail.
• KPI Strategy: Designed and implemented an EOS Level 10 (L10) executive scorecard, aligning core KPIs with operational priorities.
• Advanced Analytics: Customer segmentation, margin optimisation, and strategic growth initiatives.
• AI-Assisted Development: Leveraging Claude Code to accelerate delivery of data products, reporting pipelines, and analytics tooling.

Built the data function from scratch for 5 European businesses across tattoo, beauty, and pet grooming supply. Consolidated fragmented data sources into a unified data warehouse, enabling consistent automated reporting across all entities. This work laid the groundwork for the global structure I now lead, and resulted in my promotion to Global Lead Data Scientist.

Established the Data Science function from scratch at one of the world's largest pet-grooming supply companies. Created unified reporting across Christies and sister company Transgroom. Promoted to European Data Scientist as the business was acquired into Nexus Brands Group.

Developed bespoke image feature recognition software for biochip designs. Created technical documentation and presentations for biomedical colleagues.

My PhD project title was "Observations and Modelling of Intensity Time series for Biomedical and Astrophysical Applications". A redacted copy of my thesis is available at that link. This PhD was a joint venture between Queen's University Belfast, and Randox Laboratories to develop cutting-edge statistical techniques with application in astrophysical and biomedical analysis.

During the PhD I developed very strong data analysis skills, primarily through the development of algorithms for the large-scale statistical analysis and modelling of extremely challenging below-noise-floor signals, which were applied to astrophysical and to industrial biomedical datasets. My development and application of statistical techniques led me to novel discoveries of stellar nanoflares, which hold the potential to answer key questions about the nature of flaring in stars.

Applying those same techniques to biomedical data allowed for the development of cutting-edge noise suppression and dynamic range software. As part of this pipeline, I created bespoke image feature recognition software. This recognition software identifies key features within an image in around a second, and has led to a 98.8% reduction in downstream processing times (a saving of over 600 seconds), forming a key component of the data analysis pipeline providing competitive advantage to Randox in the field of biochip diagnostics (an industry valued at $12 billion). These results highlight the direct applications of my astrophysical data analysis skills to industrial needs.

Written and spoken communication skills were extensively developed, through academic publications, annotated code pipelines, and industrial technical documents, as well as presenting at numerous conferences, workshops and catchup meetings with industry partners. The synthesis of industry and academic experience has allowed me to develop excellent communication skills, and the opportunity to collaborate with biomedical colleagues from a range of different skill-sets and job-roles. I developed extensive coding experience, primarily working in IDL, as well as Python. I also worked as a Level 1 and 2 Lab demonstrator. I greatly enjoyed the opportunity to teach and work with large groups of students (40+), and I consistently achieved excellent feedback in student evaluation forms.

Role required leading an investigation into a manufacturing defect. Required strong interpersonal skills, in order to interact with team members and deliver the changes required. Emphasis on presenting findings to management, requiring confidence and strong presentation skills. Developed my personal responsibility, as I undertook self directed tasks to solve this manufacturing problem. Gained experience with R, as well as Six-sigma manufacturing philosophy.