CV

Led modelling strategy for Model Driven Generosity, including contextual multi-armed bandits and Double ML frameworks
Advised cross-functional teams (CRM, Marketing, MLOps) on Media Mix Modelling and Customer Retention analytics
Championed scientific excellence via peer review process, CRISP-DM adoption, and model development lifecycle improvements
Spearheaded MLOps initiatives: model monitoring, data drift detection, and feature store design
Built and deployed a Streamlit app for ABatar, enabling analysts to run A/B tests via a no-code interface
Hired, mentored, and guided data scientists and analysts across teams

Built predictive and prescriptive models (e.g. Generosity Sensitivity, Share-of-Wallet) to optimise marketing offers
Designed and deployed company-wide experimentation platform ABatar
Developed and productionised internal utility module berserker to streamline DS workflows
Awarded Best-in-Ground for outstanding contribution

Developed attribution and purchase propensity models to drive targeted marketing
Forecasted customer lifetime value using parametric model fitting techniques
Delivered 10+ deployed ML/statistical models (e.g. churn prevention, CSAT, repeat purchase), achieving >5% incremental uplift
Used Monte Carlo simulations to validate game logic for regulatory compliance

Created a novel clustering algorithm for noisy, high-dimensional astronomical data; published in peer-reviewed journals
Co-authored Nature article on stellar classification using neural networks
Published 40+ high-impact papers with advanced statistical methods (e.g. Bayesian inference)
Ranked in top 3% of Early Career Researchers in Astronomy (2017) by citations/publications

DS Software Engineering: Proficient in Python, with strong software carpentry practices
Data Platforms: Hands-on experience with Databricks, AWS, PostgreSQL, Tableau and PySpark
Experimental Design & Causal Inference: Subject matter expert in experimentation, Bayesian methods, causal inference, and reinforcement learning
Machine Learning & Optimization: Expert in statistical modelling, large-scale ML systems, and simulation techniques including MCMC, HMC, and more
Model Development Lifecycle: Advocate of CRISP-DM framework and MLOps integration for achieving scientific operational excellence

Currently active in a few open source python projects and a couple of pro-bono analytics work.

For the full list of my papers please visit arXiv.