I've been working in data science for over five years, with a specialisation in LSTM- and Transformer-based time series forecasting in Python. My most recent two year remote consulting engagement was with a Fortune 500 insurance company in the USA on a very successful project forecasting their 80M inbound calls across 2000 call types / time series. I have also worked with boosted trees, automatic feature selection, automatic hyperparameter optimisation, and clustering with K-Means, K-Modes and DBScan. Recently I’ve been collaborating with the preeminent researcher, Prof. Spyros Makridakis, founder of the Journal of Forecasting. By combining his experience in statistical forecasting and my data science skills we've just submitted a paper to PloS One that describes a new technique I designed and implemented for large-scale ensemble forecasting using meta-learning in Apache MXNet.
Prior to returning to data science I was employed as a Site Reliability Engineer, and so have a broad understanding of the engineering required in the construction of performant and scalable data models. I’ve worked in a range of data engineering roles, supporting data scientists and back-end software devs providing the linkage between production data sources and back-office data lakes. I have over 15 years experience with Linux, including complete Agile SDLC experience of taking models running on a laptop to delivering a production API. This makes me ideal for a start-up environment where people are required to "wear many hats" to get the job done.
Thank you for your time and consideration, Gary Mulder
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