Career

Takeaways from an interview with Patty McCord on The Startup Podcast.

Summary of the main messages from the book The Passion Economy by Adam Davidson.

Questions that prospective data specialists and engineers should ask about development processes before accepting a startup role.

Three essential questions to understand where an organisation stands when it comes to Data & AI (with zero hype).

Overview of the book The Economy of Algorithms by Marek Kowalkiewicz.

Eight questions that prospective data-to-AI employees should ask about a startup’s work and data culture.

Ten questions that prospective employees should ask about a startup’s team, especially for data-centric roles.

Fourteen questions that prospective employees should ask about a startup’s business model and product, especially for data-focused roles.

Reflections on what it takes to package expertise and deliver timely, actionable advice outside the context of employee relationships.

An high-level overview of things I learned from Justin Welsh’s LinkedIn Operating System course.

The story of how I joined Work on Climate as a volunteer and became its data tech lead, with lessons applied to consulting & fractional work.

My key takeaways from reading Fundamentals of Data Engineering by Joe Reis and Matt Housley.

I put the book to use after the first listen, and will definitely revisit it in the future to form better habits.

Jonathan Stark makes a compelling argument why you should have the three Cs before quitting your job to go solo consulting.

Advice for hiring a startup’s first data person: match skills to business needs, consider contractors, and get help from data people.

When focusing on a market segment defined by personal beliefs, it’s often fine to position yourself as a generalist in your craft.

Repeated exposure to media personas creates relationships that help justify premium fees.

With the commodification of data scientists, the problem of positioning has become more common: My takeaways from Genevieve Hayes interviewing Jonathan Stark.

Video and summary of a talk I gave at DataEngBytes Brisbane on what I learned from doing data engineering as part of every data science role I had.

It turns out that problems like finding a niche and defining the ideal clients are key to any solo business.

While I found the story of Gumroad interesting, The Minimalist Entrepreneur seems to over-generalise from the founder’s experience.

A summary of the second chapter of Rob Walling’s Start Small, Stay Small, along with my thoughts & reflections.

A summary of the first chapter of Rob Walling’s Start Small, Stay Small, along with my thoughts & reflections.

Yes, data science projects have suffered from classic software engineering mistakes, but the field is maturing with the rise of new engineering roles.

Exploring the hackability of speed-based coding tests, using CodeSignal’s Industry Coding Framework as a case study.

Bing Chat recently quipped that humans are small language models. Here are some of my thoughts on how we small language models can remain relevant (for now).

Discussing my recent career move into climate tech as a way of doing more to help mitigate dangerous climate change.

Back-dated meta-post that gathers my posts on Automattic blogs into a summary of the work I’ve done with the company.

Sharing remote teamwork insights, my climate & sustainability activism, Reef Life Survey publications, and progress on Automattic’s Experimentation Platform.

Being a data scientist can sometimes feel like a race against software commodities that replace interesting work. What can one do to remain relevant?

Video of a talk I gave on remote data science work at the Data Science Sydney meetup.

Discussing the pluses and minuses of remote work eighteen months after joining Automattic as a data scientist.

Frequently asked questions by visitors to this site, especially around entering the data science field.

I wanted a well-paid data science-y remote job with an established company that offers a good life balance and makes products I care about. I got it eventually.

An overview of my PhD in data science / artificial intelligence. Thesis title: Text Mining and Rating Prediction with Topical User Models.