Dr. Christopher Dillon

Made the case for a Global Head of Data role — arguing the group needed someone whose remit was to lift data literacy and decision-making maturity across every function, not just produce reports — then built the strategy, architecture and team into it. The function has been central to a major global restructuring and optimisation programme, with governance expectations appropriate for a PE-backed business preparing for further growth.

• Strategic leadership: Owned the enterprise data strategy end-to-end and positioned data insights as a core differentiator. Built ROI cases for data infrastructure investment that were approved at group level, and defined the metrics for data quality, accessibility, and utilisation across the business.
• Cross-functional enablement: Worked directly with heads of department across all 18 Nexus businesses — sitting with each to understand the actual problems they were trying to solve before building anything. Embedded in the commercial team today; previously partnered directly with Finance on reporting, forecasting models, and business performance analytics; extensive work with Operations on OTIF, inventory, and supply visibility; supported Sales, customer success and people functions with self-service analytics. Delivered executive dashboards including EOS L10 scorecards at the leadership-team level.
• Adoption & utilisation: Tracked dashboard adoption and reporting usage as first-class metrics — not just "is the data correct" but "is it actually changing decisions." Used those signals to retire low-value reports, double down on high-impact ones, and lift data literacy.
• Architecture & pipelines: Designed and built a centralised cloud data warehouse with hourly ELT pipelines from multiple NetSuite instances, Shopify, and Google. Replaced all legacy reporting with a single modern stack — 20+ dashboards, Entra SSO and role-based access, retired multiple incumbent BI tools.
• Governance, GDPR & access: Established the group's data governance posture — RBAC via Entra, data classification and self-service patterns appropriate for GDPR compliance, and lightweight metadata management so colleagues can find and trust data without going through the data team. Got the unified view into the hands of every employee at zero cost per seat.
• AI / ML in production: Drove AI adoption group-wide and embedded LLM-assisted development inside the data team (Claude Code) for faster pipeline and dashboard delivery. Stood up self-hosted local LLMs for privacy-preserving, zero-marginal-cost workloads — review sentiment, SEO copy analysis, product description harmonisation — with prompts tuned per task.
• Analytics depth: CAC modelling and optimisation, discount dependency analysis, basket & bundle intelligence, pricing and shipping margin modelling with statistical significance testing, vendor spend analysis.
• People leadership: Hired and now lead a small team of PhD-level data scientists and engineers supporting senior leadership across the group. Focused on developing the individuals on the team and building deliberate capability — not padding the org chart.
• Business impact: +25% OTIF; identified £Xm (figure withheld) in excess & obsolete inventory; recovered ~1% of revenue previously lost to out-of-stock; directly informed the group's global restructuring.

Stood up the data function for 5 European businesses spanning supply-chain trading and distribution. Consolidated fragmented source systems into a unified warehouse and delivered consistent automated reporting across all entities — promoted to Global Lead based on this work.

Established the data function from scratch at a global B2B distribution business. Unified reporting across Christies and sister supply-chain operation Transgroom. Promoted as the business was acquired into Nexus Brands Group.

Post-doctoral position handing my PhD ML image-recognition work into Randox's production diagnostics pipeline — a heavily regulated, quality-managed environment, with Randox's food-supply-chain testing division (residue, contaminant and food-safety diagnostics) operating alongside the clinical side I worked on.

PhD project: Observations and Modelling of Intensity Time series for Biomedical and Astrophysical Applications — joint venture with 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 ML-based image feature recognition now embedded in Randox's production biochip pipeline — 98.8% reduction in downstream processing time in a $12B diagnostics market. Redacted thesis.

Seagate bursary recipient. Head of peer mentor scheme.