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Miles McBain
Statistician / Developer
Miles McBain is an accomplished Data Scientist at The Data Collective, based in Australia. He specializes in applied statistics and software development, focusing on data analysis pipeline engineering and enhancing workflows within data science teams. His expertise lies in creating efficient and reusable code, which he has applied across various sectors, including emergency services and nonprofit organizations.15
Professional Background
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Education and Early Career: Miles has a background in statistics and computing, which he leverages to help organizations better understand their business and customers. He has worked with notable organizations such as Queensland Fire and Emergency Services and CSIRO, contributing significantly to data science practices within these institutions.1
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Current Role: At The Data Collective, Miles plays a crucial role in helping charities utilize data effectively. His work emphasizes the importance of reproducibility in data science, advocating for practices that foster collaboration and skill-sharing among team members.24
Contributions to the Field
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Open Source and Community Engagement: Miles is an active contributor to the open-source community, particularly in the R programming environment. He is known for his discussions on best practices in data analysis, including the design of reusable code packages and the management of data science projects.13
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Thought Leadership: He has presented on various topics related to data analysis, including patterns and anti-patterns in code reuse. His insights aim to bridge the gap between data science, engineering, and software development, promoting a more structured approach to data analysis workflows.13
In summary, Miles McBain is a key figure in the Australian data science landscape, recognized for his innovative approaches to data analysis and his commitment to improving team workflows through effective coding practices.
Highlights
I am going to try syndicating some of my blog posts to here, since #rstats and #datascience people seem to be pretty active lately.
My latest: https://lnkd.in/gb5S_SPC
This is part of the 'Data Science Engineering' topic I've been riffing on. I'm trying to talk to what I see as the difference between building a data analysis, and building the capability to reliably perform that kind of analysis in a predictable way.
One way you can tell a team hasn't invested enough in capability is if their capacity is exhausted by executing and re-executing the same old kinds of data analysis that have become 'BAU'.
Please share this blog post with anyone on the hunt for a Data Science/Analyst role. There are a lot of companies out there with no clue how to hire or manage this function, and working for them won't be a fun time, especially for someone new in the field. Here's how to avoid them:
