Thomas Trainor-Gilham

Lead marketing measurement and experimentation for clients including WorldRemit (Zepz), Global Media, Healf, and others across fintech, DTC and media. • Build and refresh Bayesian and Frequentist Marketing Mix Models (MMM) using libraries such as Meridian, PyMC, and Robyn to mathematically isolate the true incremental ROI of marketing spend. • Execute retrospective campaign analysis for online and offline media (OOH, Radio) using CausalImpact, Synthetic Control, DiD, GeoLift, etc to quantify incremental lift against counterfactual baselines, producing iROAS and cost-per-incremental-order metrics that gave clients a commercially defensible case for offline spend. • Bayesian posterior distributions and model uncertainty into clear strategic recommendations for non-technical stakeholders turning measurement outputs into budget decisions that drive effficiency gains.

Embedded measurement consultant working with Global's SME Development team to quantify the incremental impact of OOH and radio campaigns. • Design and run synthetic control experiments to measure causal lift in awareness and sales metrics across Global's advertising clients. • Produce iROAS, cost-per-incremental-order and brand response metrics that give clients a commercially defensible case for offline media investment. • Translate model outputs and uncertainty estimates into clear campaign execution recommendations for non-technical commercial stakeholders.

• Implemented Marketing Mix Modeling (MMM) to quantify the effectiveness and ROI of diverse marketing channels (e.g., TV, digital, print) for major accounts including Co-op, Bupa, and Harley Davidson. • Developed a Gradient Boosting Decision Trees age inference model (LightGBM-based) using GA4 data to predict customer age segments based on behavioural features (e.g. spend patterns, time-of-day preferences, brand loyalty). • Leveraged model outputs to inform segmentation, campaign design A/B testing, and geo-targeting strategies tailored to younger customer cohorts to optimise client advertising spend and improve campaign effectiveness. • Partnered with marketing, media & client stakeholders to translate & present findings into actionable business strategies.

Retail Analytics Consultant within the Category and Analytics division at Kantar taking working across Technology and Consulting to deliver actionable insights and interfacing with multiple data sources including Scan/Point of Sale and attitudinal data to build predictive models & relevant insights across range & assortment as well as Price/Pack architecture. Key Responsibilities: • Develop predictive analytical models within existing Kantar frameworks to address client business questions • Use models & tools to deliver analytical advisory and client service including DB management, software testing, leading training sessions & running projects on behalf of clients. • Collect, interpret, and analyse data using Python, Excel, R, SQL, as well as in-house software, and statistical methods.

Taught GCSE & A-Level Mathematics and Economics

- Acted as an assistant/ under-study to Mr. Sandeep Shresthra, Program Director of Dolma Foundation, as he met and formed relationships with strategic partners. - I was able to gain first-hand experience with NGOs and growth companies in Nepal, and bear witness to how foreign direct investment is stimulated and brought into Nepal. - I was able to accompany The Dolma Impact Fund/ Foundation team on a company trip to Bridim, a remote village in the Langtang National Park, to witness the work of the foundation.

Modules: • Empirical Industrial Organisation: (100% Achieved); Production/demand functions, Maximum Likelihood Estimators, & entry games. • Machine Learning: (90%); LDA, QDA, KNN, cross-validation, regularisation, PCA, Random Forests, Boosting, SVMs, neural networks. • Applied Financial Econometrics: (77% Achieved); Stationarity, threshold models, ARMA, VAR, VECM, cointegration, ARCH / GARCH • Large Scale Data Engineering: (85% Achieved); AWS cloud architecture best practices, cloud economics, Hadoop, Apache Spark, DevOps

Achieved First-class Honours (72%) with chosen modules heavily focused on Econometrics, Microeconomics, and statistics.

Mathematics - A* Economics - A* Physics - A