Dr. Christopher Dillon

Advocated for and created the Global Lead Data Scientist role, then built the data strategy, infrastructure, and team from scratch across 18 businesses. This function has been central to a major global restructuring and optimisation programme — providing the evidence base for strategic decisions across the group.

• Team: Built and lead a team of PhD-level data scientists and engineers supporting senior leadership across the group.
• Platform: Designed and built a centralised data warehouse and 20+ dashboard reporting solution — syncing hourly from multiple NetSuite instances, Shopify, and Google. Replaced all legacy reporting with a single Streamlit stack with Entra SSO and role-based access.
• Analytics: Customer acquisition cost modelling and optimisation, discount dependency analysis, basket & bundle intelligence, pricing and shipping margin modelling with statistical significance testing, vendor spend analysis. EOS L10 scorecards across all departments.
• Business impact: Created a unified global data view and got it into the hands of every employee at zero cost per seat, enabling: +25% OTIF; identified £Xm (figure withheld) in excess & obsolete inventory; recovered ~1% of revenue previously lost to out-of-stock; surfaced these to support growth and directly inform the global restructuring.

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.

Post-doctoral position to hand over the biomedical image recognition work from my PhD into Randox's production pipeline. Developed bespoke feature recognition software for biochip designs and created technical documentation for biomedical colleagues.

PhD project: Observations and Modelling of Intensity Time series for Biomedical and Astrophysical Applications — joint venture between Queen's and Randox. Developed statistical techniques for below-noise-floor signals, applied to both stellar lightcurves and biochip diagnostics. Led to novel discoveries of stellar nanoflares (published in ApJ) and to image feature recognition software now embedded in Randox's biochip pipeline — 98.8% reduction in downstream processing time in a $12B diagnostics market. Primarily IDL and Python. Redacted thesis.