Summary
AI/ML Specialist with 3+ years of experience building production-scale AI systems. Proficient in Python, PyTorch, and MLOps. Consistently delivers robust, scalable solutions that drive business value. Strong academic background in ML with proven ability to optimise inference pipelines and collaborate with engineering teams to deploy impactful ML applications.
Work Experience
Clarity AI
Machine Learning Engineer, 11/2022 - 11/2024
- Deployed scalable NLP News Analysis Pipeline, reducing inference time by 85% and achieved 99% reliability, 300k daily articles.
- Improved news analysis with Generative AI, leveraging LLMs with the LangChain framework, reducing false positives by 80%.
- Developed live model monitoring with a dashboard and notification system, enabling proactive retraining and rapid issue detection.
- Collaborated with cross-functional engineering teams to implement automated testing and CI/CD pipelines for model deployment, mentoring junior developers on best practices.
- Automated data quality validation, enhancing data reliability and delivering more accurate client insights.
London Stock Exchange Group, LSEG Labs
Data Scientist, 11/2021 - 11/2022
- Developed market impact prediction model, incorporating model explainability to boost user confidence in recommendations.
- Collaborated with engineering teams to establish MLOps practices, leveraging MLflow and FastAPI to improve model deployment.
- Participated in industry sessions on responsible AI, and contributed to university outreach and mentorship programs.
- Awarded best project in internal hackathon for developing a blockchain solution focused on Tokenised Derivatives Contracts.
Optimal Compliance & Novel R&D
Lead R&D Consultant & Co-Founder, 09/2018 - 11/2021
- Identified opportunity and led the development of Novel R&D, a SaaS platform automating R&D tax processes, using Agile methodologies to rapidly iterate from MVP to successful spin-out, resulting in significant reduction in client processing time.
Education
University College London
MSc Machine Learning, 2020 - 2021
- Grade: Distinction - 88% GPA
- Relevant courses: Reinforcement Learning (97%), Deep Learning (98%), Natural Language Processing (88%), Applied Machine Learning (91%)
- Thesis (Distinction): Reinforcement learning framework using graph neural networks to optimise network structures. GitHub Repo
- Recognised as one of the top 5 group papers on Natural Language Processing. Research Paper.
University College London
BSc Natural Sciences (Physics/Chemistry), 2014 - 2017
- Grade: Upper Second-Class Honours
- Thesis (First Class grade): Computational Methods in Drug Discovery and Personalised Medicine.
Skills
- Programming Languages: Python (PyTorch, Pandas, Scikit-learn, HuggingFace, XGBoost), SQL, Bash/Shell
- Big Data & Visualisation: MySQL, Snowflake, Spark, Airflow, Tableau, Data Modeling
- Cloud & Tools: AWS, Docker, Kubernetes, MLflow, FastAPI, Jupyter, Git, CI/CD, Testing
- Machine Learning Expertise: A/B Testing, Model Monitoring, Explainable AI (SHAP)
- Theoretical Knowledge: Mathematics, Probability, Statistics, Algorithms
Certifications
Extra Curricular
- Waterpolo (University Men's Captain; Great Britain National Team - Under 18s)
- Other Sports: Football, Tennis, Snowsports (season in French Alps)
- Volunteering: Charity Trustee (21st Century Leaders Foundation)