Summary
Daniel Hewitt is a Senior Data Scientist and UK-based contractor who translates a PhD in computational chemistry into high-impact ML solutions for marketing and materials discovery. Over four years of research and industry experience he has built production-ready pipelines and distributed training systems (Ray/Anyscale), improved campaign response by 15%, and architected in-house data platforms that saved his employer millions. He combines strong Python engineering (pandas, numpy, plotly), ML expertise (scikit-learn, XGBoost, TensorFlow/PyTorch) and SQL/Git best practices with hands-on optimization for memory and inference speed. Comfortable mentoring juniors and bridging ML, MLE and data engineering, he has a track record of turning academic simulation workflows into transferable, commercial analytics. A less obvious strength is his domain fluency in computational materials screening, giving him a rare ability to apply scientific modelling rigor to business problems.
4 years of coding experience
1 year of employment as a software developer
University College London