Résumé

Curious Mind, Data-Driven Heart

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🔍 About Me

Machine learning engineer and deep tech enthusiast with a background in astrophysics and high-performance computing. I’ve led AI teams in startups, co-founded a deep learning hedge fund, and built ML systems that power real-world decisions. I’m passionate about foundational models, energy-efficient ML, and applying scientific thinking to solve complex problems.

🚀 Experience Highlights

Head of Data Science – Proportunity

2018 – 2024, London (Acquired)

• Built and led a team of ML engineers and data scientists from seed to acquisition.
• Developed predictive ML models for real estate price growth — 93% accuracy, powering underwriting for £20M+ in loans.
• Integrated models into GCP pipelines; directly influenced investment and risk decisions.

Co-founder & CTO – Alpha-I

2016 – 2018, London

• Founded a VC-backed deep learning hedge fund via Entrepreneur First.
• Designed Bayesian deep learning models for S&P 500 time-series forecasting.
• Raised ~$1M in seed funding; led ML research, infra, and strategy.

Postdoctoral Researcher – University College London

2013 – 2016

• Developed Bayesian inference methods to analyse cosmological data from space telescopes.
• Co-authored 20+ peer-reviewed publications.
• Supervised PhD students and published in leading astrophysics journals.

🧠 Skills & Tools

Languages: Python, C++, SQL
ML/AI: PyTorch, TensorFlow, Bayesian methods, time-series forecasting
MLOps & Infra: GCP, Airflow, MLFlow, Git, model deployment
Focus Areas: Energy-efficient ML, foundational models

📚 Education

PhD in Astrophysics – University of Cambridge
Isaac Newton Scholar; research on Bayesian inference for space telescope data.

MSc in Astrophysics (Distinction) – University College London
Thesis: Bayesian orbital estimation for exoplanet detection using MCMC; published in MNRAS.