My 10-step path to becoming a remote data scientist with Automattic

About two years ago, I read the book The Year without Pants, which describes the author’s experience leading a team at Automattic (the company behind, among other products). Automattic is a fully-distributed company, which means that all of its employees work remotely (hence pants are optional). While the book discusses some of the challenges of working remotely, the author’s general experience was very positive. A few months after reading the book, I decided to look for a full-time position after a period of independent work. Ideally, I wanted a well-paid data science-y remote job with an established distributed tech company that offers a good life balance and makes products I care about. Automattic seemed to tick all my boxes, so I decided to apply for a job with them. This post describes my application steps, which ultimately led to me becoming a data scientist with Automattic.

Before jumping in, it’s worth noting that this post describes my personal experience. If you apply for a job with Automattic, your experience is likely to be different, as the process varies across teams, and evolves over time.

📧 Step 1: Do background research and apply

I decided to apply for a data wrangler position with Automattic in October 2015. While data wrangler may sound less sexy than data scientist, reading the job ad led me to believe that the position may involve interesting data science work. This impression was strengthened by some LinkedIn stalking, which included finding current data wranglers and reading through their profiles and websites. I later found out that all the people on the data division start out as data wranglers, and then they may pick their own title. Some data wranglers do data science work, while others are more focused on data engineering, and there are some projects that require a broad range of skills. As the usefulness of the term data scientist is questionable, I’m not too fussed about fancy job titles. It’s more important to do interesting work in a supportive environment.

Applying for the job was fairly straightforward. I simply followed the instructions from the ad:

Does this sound interesting? If yes, please send a short email to jobs @ this domain telling us about yourself and attach a resumé. Let us know what you can contribute to the team. Include the title of the position you’re applying for and your name in the subject. Proofread! Make sure you spell and capitalize WordPress and Automattic correctly. We are lucky to receive hundreds of applications for every position, so try to make your application stand out. If you apply for multiple positions or send multiple emails there will be one reply.

Having been on the receiving side of job applications, I find it surprising that many people don’t bother writing a cover letter, addressing the selection criteria in the ad, or even applying for a job they’re qualified to do. Hence, my cover letter was fairly short, comprising of several bullet points that highlight the similarities between the job requirements and my experience. It was nothing fancy, but simple cover letters have worked well for me in the past.

⏳ Step 2: Wait patiently

The initial application was followed by a long wait. From my research, this is the typical scenario. This is unsurprising, as Automattic is a fairly small company with a large footprint, which is both distributed and known as a great place to work (e.g., its Glassdoor rating is 4.9). Therefore, it attracts many applicants from all over the world, which take a while to process. In addition, Matt Mullenweg (Automattic’s CEO) reviews job applications before passing them on to the team leads.

As I didn’t know that Matt reviewed job applications, I decided to try to shorten the wait by getting introduced to someone in the data division. My first attempt was via a second-degree LinkedIn connection who works for Automattic. He responded quickly when I reached out to him, saying that his experience working with the company is in line with the Glassdoor reviews – it’s the best job he’s had in his 15-year-long career. However, he couldn’t help me with an intro, because there is no simple way around Automattic’s internal processes. Nonetheless, he reassured me that it is worth waiting patiently, as the strict process means that you end up working with great people.

I wasn’t in a huge rush to find a job, but in December 2015 I decided to accept an offer to become the head of data science at Car Next Door. This was a good decision at the time, as I believe in the company’s original vision of reducing the number of cars on the road through car sharing, and it seemed like there would be many interesting projects for me to work on. The position wasn’t completely remote, but as the company was already spread across several cities, I was able to work from home for a day or two every week. In addition, it was a pleasant commute by bike from my Sydney home to the office, so putting the fully-remote job search on hold didn’t seem like a major sacrifice. As I haven’t heard anything from Automattic at that stage, it seemed unwise to reject a good offer, so I started working full-time with Car Next Door in January 2016.

I successfully attracted Automattic’s attention with a post I published on the misuse of the word insights by many tech companies, which included an example from Greg Ichneumon Brown, one of the data wranglers, commented on the post, and invited me to apply to join Automattic and help them address the issues I raised. This happened after I accepted the offer from Car Next Door, and hasn’t resulted in any speed up of the process, so I just gave up on Automattic and carried on with my life.

💬 Step 3: Chat with the data lead

I finally heard back from Automattic in February 2016 (four months after my initial application and a month into my employment with Car Next Door). Martin Remy, who leads the data division, emailed me to enquire if I’m still interested in the position. I informed him that I was no longer looking for a job, but we agreed to have an informal chat, as I’ve been waiting for such a long time.

As is often the case with Automattic interviews, the chat with Martin was completely text-based. Working with a distributed team means that voice and video calls can be hard to schedule. Hence, Automattic relies heavily on textual channels, and text-based interviews allow the company to test the written communication skills of candidates. The chat revolved around my past work experience, and Martin also took the time to answer my questions about the company and the data division. At the conclusion of the chat, Martin suggested I contact him directly if I was ever interested in continuing the application process. While I was happy with my position at the time, the chat strengthened my positive impression of Automattic, and I decided that I would reapply if I were to look for a full-time position again.

My next job search started earlier than I had anticipated. In October 2016, I decided to leave Car Next Door due to disagreements with the founders over the general direction of the company. In addition, I had more flexibility in choosing where to live, as my personal circumstances had changed. As I’ve always been curious about life outside the capital cities of Australia, I wanted to move away from Sydney. While I could have probably continued working remotely with Car Next Door, I felt that it would be better to find a job with a fully-distributed team. Therefore, I messaged Martin and we scheduled another chat.

The second chat with Martin took place in early November. Similarly to the first chat, it was conducted via Skype text messages, and revolved around my work in the time that has passed since the first chat. This time, as I was keen on continuing with the process, I asked more specific questions about what kind of work I’m likely to end up doing and what the next steps would be. The answers were that I’d be joining the data science team, and that the next steps are a pre-trial test, a paid trial, and a final interview with Matt. While this sounds straightforward, it took another six months until I finally became an Automattic employee (but I wasn’t in a rush).

☑️ Step 4: Pass the pre-trial test

The pre-trial test consisted of a data analysis task, where I was given a dataset and a set of questions to answer by Carly Stambaugh, the data science lead. The goal of the test is to evaluate the candidate’s approach to a problem, and assess organisational and communication skills. As such, the focus isn’t on obtaining a specific result, so candidates are given a choice of several potential avenues to explore. The open-ended nature of the task is reminiscent of many real-world data science projects, where you don’t always have a clear idea of what you’re going to discover. While some people may find this kind of uncertainty daunting, I find it interesting, as it is one of the things that makes data science a science.

I spent a few days analysing the data and preparing a report, which was submitted as a Jupyter Notebook. After submitting my initial report, there were a few follow-up questions, which I answered by email. The report was reviewed by Carly and Martin, and as they were satisfied with my work, I was invited to proceed to the next stage: A paid trial project.

👨‍💻 Step 5: Do the trial project

The main part of the application process with Automattic is the paid trial project. The rationale behind doing paid trials was explained a few years ago by Matt in Hire by Auditions, Not Resumes:

Before we hire anyone, they go through a trial process first, on contract. They can do the work at night or over the weekend, so they don’t have to leave their current job in the meantime. We pay a standard rate of $25 per hour, regardless of whether you’re applying to be an engineer or the chief financial officer.

During the trials, we give the applicants actual work. If you’re applying to work in customer support, you’ll answer tickets. If you’re an engineer, you’ll work on engineering problems. If you’re a designer, you’ll design.

There’s nothing like being in the trenches with someone, working with them day by day. It tells you something you can’t learn from resumes, interviews, or reference checks. At the end of the trial, everyone involved has a great sense of whether they want to work together going forward. And, yes, that means everyone — it’s a mutual tryout. Some people decide we’re not the right fit for them.

The goal of my trial project was to improve the Elasticsearch language detection algorithm. This took about a month, and ultimately resulted in a pull request that got merged into the language detection plugin. I find this aspect of the process pretty exciting: While the plugin is used to classify millions of documents internally by Automattic, its impact extends beyond the company, as Elasticsearch is used by many other organisations and projects. This stands in contrast to many other technical job interviews, which consist of unpaid work on toy problems under stressful conditions, where the work performed is ultimately thrown away. While the monetary compensation for the trial work is lower than the market rate for data science consulting, I valued the opportunity to work on a real open source project, even if this hadn’t led to me getting hired.

There was much more to the trial project than what’s shown in the final pull request. Most of the discussions were held on an internal project thread, primarly under the guidance of Carly (the data science lead), and Greg (the data wrangler who replied to my post a year earlier). The project was kicked off with a general problem statement: There was some evidence that the Elasticsearch language detection plugin doesn’t perform well on short texts, and my mission was to improve it. As the plugin didn’t include any tests for short texts, one of the main contributions of my work was the creation of datasets and tests to measure its accuracy on texts of different lengths. This was followed by some tweaks that improved the plugin’s performance, as summarised in the pull request. Internally, this work consisted of several iterations where I came up with ideas, asked questions, implemented the ideas, shared the results, and discussed further steps. There are still many possible improvements to the work done in the trial. However, as trials generally last around a month, we decided to end it after a few iterations.

I enjoyed the trial process, but it is definitely not for everyone. Most notably, there is a strong emphasis on asynchronous text-based communication, which is the main mode by which projects are coordinated at Automattic. People who don’t enjoy written communication may find this aspect challenging, but I have always found that writing helps me organise my thoughts, and that I retain information better when reading than when listening to people speak. That being said, Automatticians do meet in person several times a year, and some teams have video chats for some discussions. While doing the trial, I had a video chat with Carly, which was the first (and last) time in the process that I got to see and hear a live human. However, this was not an essential part of the trial project, as our chat was mostly on the data scientist role and my job expectations.

⏳ Step 6: Wait patiently

I finished working on the trial project just before Christmas. The feedback I received throughout the trial was positive, but Martin, Carly, and Greg had to go through the work and discuss it among themselves before making a final decision. This took about a month, due to the holiday period, various personal circumstances, and the data science team meetup that was scheduled for January 2017. Eventually, Martin got back to me with positive news: They were satisfied with my trial work, which meant there was only one stage left – the final interview with Matt Mullenweg, Automattic’s CEO.

👉 Step 7: Ping Matt

Like other parts of the process, the interview with Matt is text-based. The way it works is fairly simple: I was instructed to message Matt on Slack and wait for a response, which may take days or weeks. I sent Matt a message on January 25, and was surprised to hear back from him the following morning. However, that day was Australia Day, which is a public holiday here. Therefore, I only got back to him two hours after he messaged me that morning, and by that time he was probably already busy with other things. This was the start of a pretty long wait.

⏳ Step 8: Wait patiently

I left Car Next Door at the end of January, as I figured that I would be able to line up some other work even if things didn’t work out with Automattic. My plan was to take some time off, and then move up to the Northern Rivers area of New South Wales. I had two Reef Life Survey trips planned, so I wasn’t going to start working again before mid-April. I assumed that I would hear back from Matt before then, which would have allowed me to make an informed decision whether to look for another job or not.

After two weeks of waiting, the time for my dive trips was nearing. As I was going to be without mobile reception for a while, I thought it’d be worth letting Matt know my schedule. After discussing the matter with Martin, I messaged Matt. He responded, saying that we might as well do the interview at the beginning of April, as I won’t be starting work before that time anyway. I would have preferred to be done with the interview earlier, but was happy to have some certainty and not worry about missing more chat messages before April.

In early April, I returned from my second dive trip (which included a close encounter with Cyclone Debbie), and was hoping to sort out my remote work situation while completing the move up north. Unfortunately, while the move was successful, I was ready to give up on Automattic because I haven’t heard back from Matt at all in April. However, Martin remained optimistic and encouraged me to wait patiently, which I did as I was pretty busy with the move and with some casual freelancing projects.

💬 Step 9: Chat with Matt and accept the job offer

The chat with Matt finally happened on May 2. As is often the case, it took a few hours and covered my background, the trial process, and some other general questions. I asked him about my long wait for the final chat, and he apologised for me being an outlier, as most chats happen within two weeks of a candidate being passed over to him. As the chat was about to conclude, we got to the topic of salary negotiation (which went well), and then the process was finally over! Within a few hours of the chat I was sent an offer letter and an employment contract. As Automattic has an entity in Australia (called Ausomattic), it’s a fairly standard contract. I signed the contract and started work the following week – over a year and a half after my initial application. Even before I started working, I booked tickets to meet the data division in Montréal – a fairly swift transition from the long wait for the final interview.

🎉 Step 10: Start working and choose a job title

As noted above, Automatticians get to choose their own job titles, so to become a data scientist with Automattic, I had to set my job title to Data Scientist. This is generally how many people become data scientists these days, even outside Automattic. However, job titles don’t matter as much as job satisfaction. And after 2.5 months with Automattic, I’m very satisfied with my decision to join the company. My first three weeks were spent doing customer support, like all new Automattic employees. Since then, I’ve been involved in projects to make engagement measurement more consistent (harder than it sounds, as counting things is hard), and to improve the data science codebase (e.g., moving away from Legacy Python). Besides that, I also went to Montréal for the data division meetup, and have started getting into chatbot work. I’m looking forward to doing more work and sharing my experience here and on

Ask Why! Finding motives, causes, and purpose in data science

Some people equate predictive modelling with data science, thinking that mastering various machine learning techniques is the key that unlocks the mysteries of the field. However, there is much more to data science than the What and How of predictive modelling. I recently gave a talk where I argued the importance of asking Why, touching on three different topics: stakeholder motives, cause-and-effect relationships, and finding a sense of purpose. A video of the talk is available below. Unfortunately, the videographer mostly focused on me pacing rather than on the screen, but you can check out the slides here (note that you need to use both the left/right and up/down arrows to see all the slides).

If you’re interested in the topics covered in the talk, here are a few posts you should read.

Stakeholders and their motives

Causality and experimentation

Purpose, ethics, and my personal path

Cover image: Why by Ksayer

Whitetip shark with an RLS transect

The joys of offline data collection

Many modern data scientists don’t get to experience data collection in the offline world. Recently, I spent a month sailing down the northern Great Barrier Reef, collecting data for the Reef Life Survey project. In addition to being a great diving experience, the trip helped me obtain general insights on data collection and machine learning, which are shared in this article.

The Reef Life Survey project

Reef Life Survey (RLS) is a citizen scientist project, led by a team from the University of Tasmania. The data collected by RLS volunteers is freely available on the RLS website, and has been used for producing various reports and scientific publications. An RLS survey is performed along a 50 metre tape, which is laid at a constant depth following a reef’s contour. After laying the tape, one diver takes photos of the bottom at 2.5 metre intervals along the transect line. These photos are automatically analysed to classify the type of substrate or growth (e.g., hard coral or sand). Divers then complete two swims along each side of the transect. On the first swim (method 1), divers record all the fish species and large swimming animals found in a 5 metre corridor from the line. The second swim (method 2) requires keeping closer to the bottom and looking under ledges and vegetation in a 1 metre corridor from the line, targeting invertebrates and cryptic animals. The RLS manual includes all the details on how surveys are performed.

Performing RLS surveys is not a trivial task. In the tropics, it is not uncommon to record around 100 fish species on method 1. The scientists running the project are very conscious of the importance of obtaining high-quality data, so training to become an RLS volunteer takes considerable effort and dedication. The process generally consists of doing surveys together with an experienced RLS diver, and comparing the data after each dive. Once the trainee’s data matches that of the experienced RLSer, they are considered good enough to perform surveys independently. However, retraining is often required when surveying new ecoregions (e.g., an RLSer trained in Sydney needs further training to survey the Great Barrier Reef).

RLS requires a lot of hard work, but there are many reasons why it’s worth the effort. As someone who cares about marine conservation, I like the fact that RLS dives yield useful data that is used to drive environmental management decisions. As a scuba diver, I enjoy the opportunity to dive places that are rarely dived and the enhanced knowledge of the marine environment – doing surveys makes me notice things that I would otherwise overlook. Finally, as a data scientist, I find the exposure to the work of marine scientists very educational.

Pre-training and thoughts on supervised learning

Doing surveys in the tropics is a completely different story from surveying temperate reefs, due to the substantially higher diversity and abundance of marine creatures. Producing high-quality results requires being able to identify most creatures underwater, while doing the survey. It is possible to write down descriptions and take photos of unidentified species, but doing this for a large number of species is impractical.

Training the neural network in my head to classify tropical fish by species was an interesting experience. The approach that worked best was making flashcards using reveal.js, photos scraped from various sources, and past survey data. As the image below shows, each flashcard consists of a single photo, and pressing the down arrow reveals the name of the creature. With some basic JavaScript, I made the presentation select a different subset of photos on each load. Originally, I tried to learn all the 1000+ species that were previously recorded in the northern Great Barrier Reef, but this proved to be too hard – I realised that a better strategy was needed. The strategy that I chose was to focus on the most frequently-recorded species: I started by memorising the most frequent ones (e.g., those recorded on more than 50% of surveys), and gradually made it more challenging by decreasing the frequency threshold (e.g., to 25% in 5% steps). This proved to be pretty effective – by the time I started diving I could identify about 50-100 species underwater, even though I had mostly been using static images. It’d be interesting to know whether this kind of approach would be effective in training neural networks (or other batch-trained models) in certain scenarios – spend a few epochs training with instances from a subset of the classes, and gradually increase the number of considered classes. This may be effective when errors on certain classes are more important than others, and may yield different results from simply weighting classes or instances. Please let me know if you know of anyone who has experimented with this idea (update: gwern from Reddit pointed me to the paper Curriculum Learning by Bengio et al., which discusses this idea).

RLS flashcard example (Chaetodon lunulatus)

RLS flashcard example (Chaetodon lunulatus)

While repeatedly looking at photos and their labels felt a lot like training an artificial neural network, as a human I have the advantage of being able to easily use information from multiple sources. For example, fish ID books such as Reef Fish Identification: Tropical Pacific provide concise descriptions of the identifying physical features of each fish (see the image below for the book’s entry for Chaetodon lunulatus – the butterflyfish from the flashcard above). Reading those descriptions made me learn more effectively, by helping me focus my attention on the parts that matter for classification. Learning only from static images can be hard when classifying creatures with highly variable colour schemes – using extraneous knowledge about what actually matters when it comes to classification is the way to go in practice. Further, features that are hard to decode from photos – like behaviour and habitat – are sometimes crucial to distinguishing different species. One interesting thought is that while photos can be seen as raw data, natural language descriptions are essentially models. Utilising such models is likely to be of benefit in many areas. For example, being able to tell a classifier what to look for in an image would make training a supervised classifier more similar to the way humans learn. This may be achieved using similar techniques to those used for generating image descriptions, except that the goal would be to use descriptions of the classes to improve classification accuracy.

Fish ID example (Chaetodon lunulatus)

Fish ID example (Chaetodon lunulatus). Source: Reef Fish Identification: Tropical Pacific

Another difference between my learning and supervised machine learning is that if I found a creature hard to identify, I would go and look for more photos or videos of them. Videos were especially valuable, because in practice I rarely had to identify static creatures. This approach may be applicable in situations where labelled data is abundant. Sometimes, using all the labelled data makes model training too slow to be practical. An approach I used in the past to overcome this issue is to randomly sample the data, but it often makes sense to sample in a way that yields the best model, e.g., by sampling more instances from classes that are harder to classify.

One similarity to supervised machine learning that I encountered was the danger of overfitting. Due to the relatively small number of photos and the fact that I had to view each one of them multiple times, I found that in some cases I memorised the entire photo rather than the creature. This was especially the case with low-quality photos or ones that were missing key features. My regularisation approach consisted of trying to memorise the descriptions from the book, and collecting more photos. I wish more algorithms were this self-conscious about overfitting!

Can’t this be automated?

While doing surveys and studying species, I kept asking myself whether the whole thing can be automated. Thanks to deep learning, computers have recently gotten very good at classifying images, sometimes outperforming humans. It seems likely that at some point the survey methodology would be changed to just taking a video of the dive, and letting an algorithm do the hard job of identifying the creatures. Analysis of the bottom photos is automated, so it is reasonable to automate the other survey methods as well. However, there are quite a few challenges that need to be overcome before full automation can be implemented.

If the results of the LifeCLEF 2015 Fish Task are any indication, we are quite far from automating fish identification. The precision of the top methods in that challenge was around 80% for identifying 15 fish species from underwater videos, where the chosen species are quite distinct from each other. In tropical surveys it is not uncommon to record around 100 fish species along the 50 metre transect, with many species being similar to each other. It’s usually the case that it’s not same species on every dive (even at the same site), so replacing humans would require training a highly accurate classifier on thousands of species.

Dealing with high diversity isn’t the only challenge in automating RLS. The appearance of many species varies by gender and age, so the classifier would have to learn all those variations (see image below for an example). Getting good training data can be very challenging, since the labelling process is labour-intensive, and elements like colour and backscatter are highly dependent on dive site conditions and the quality of the camera. Another complication is that RLS data includes size estimates, which can be hard to obtain from videos and photos without knowing how far the camera was from the subject and the type of lens used. In addition, accounting for side information (geolocation, behaviour, depth, etc.) can make a huge difference in accurately identifying species, but it isn’t easy to integrate with some learning models. Finally, it is likely that some species will be missed when videos are taken without any identification done underwater, because RLSers tend to get good photos of species that they know will be hard to identify, even if it means spending more time at one spot or shining strobes under ledges.

Chlorurus sordidus variations

Chlorurus sordidus variations. Source: Tropical Marine Fishes of Australia

Another aspect of automating surveys is completely removing the need for human divers by sending robots down. This is an active research area, and is the only way of surveying deep waters. However, this approach still requires a boat-based crew to deploy the robots. It may also yield different data from RLS for cryptic species, though this depends on the type of robots used. In addition, there’s the issue of cost – RLS relies on volunteer scuba divers who are diving anyway, so the cost of getting RLSers to do surveys is rather low (especially for shore dives near a diver’s home, where there is no cost to RLS). Further, RLS’s mission is “to inspire and engage a global volunteer community to survey reefs using scientific methods and share knowledge about marine ecosystem health”. Engaging the community is a crucial part of RLS because robots do not care about the environment. Humans do.

Small data is valuable

When compared to datasets commonly encountered online, RLS data is small. As the image below shows, fewer than 10,000 surveys have been conducted to date. However, this data is still valuable, as it provides a high-quality snapshot of the state of marine ecosystems in areas that wouldn’t be surveyed if it wasn’t for RLS volunteers. For example, in a recent Nature article, the authors used RLS data to assess the vulnerability of marine fauna to global warming.

RLS surveys by Australian financial year (July-June)

RLS surveys by Australian financial year (July-June). Source: RLS Foundation Annual Report 2015

Each RLS survey requires several hours of work. In addition to performing the survey itself, a lot of work goes into entering the data and verifying its quality. Getting to the survey sites is not always a trivial task, especially for remote sites such as some of those we dived on my recent trip. Spending a month diving the Great Barrier Reef is a good way of appreciating its greatness. As the map shows, the surveys we did covered only the top part of the reef’s 2300 kilometres, and we only sampled a few sites within that part. The Great Barrier Reef is very vast, and it is hard to convey its vastness with just words or a map. You have to be there to understand – it is quite humbling.

In summary, the RLS experience has given me a new appreciation for small data in the offline world. Offline data collection is often expensive and labour-intensive – you need to work hard to produce a few high-quality data points. But the size of your data doesn’t matter (though having more quality data is always good). What really matters is what you do with the data – and the RLS team and their collaborators have been doing quite a lot. The RLS experience also illustrates the importance of domain expertise: I’ve looked at the RLS datasets, but I have no idea what questions are worth asking and answering using those datasets. The RLS project is yet another example of how in science collecting data is time-consuming, and coming up with appropriate research questions is hard. It is a lot of fun, though.

Start of Overland track

The long road to a lifestyle business

Almost a year ago, I left my last full-time job and decided to set on an independent path that includes data science consulting and work on my own projects. The ultimate goal is not to have to sell my time for money by generating enough passive income to live comfortably. My five main areas of focus are – in no particular order – personal branding & networking, data science contracting, Bandcamp Recommender, Price Dingo, and marine conservation. This post summarises what I’ve been doing in each of these five areas, including highlights and lowlights. So far, it’s way better than having a “real” job. I hope this post will help others who are on a similar journey (there seem to be more and more of us – I’d love to hear from you).

Personal branding & networking

Finding clients requires considerably more work than finding a full-time job. As with job hunting, the ideal situation is where people come to you for help, rather than you chasing them. To this end, I’ve been networking a lot, giving talks, writing up posts and working on distributing them. It may be harder than getting a full-time job, but it’s also much more interesting.

Highlights: going viral in China, getting a post featured in KDNuggets
Lowlights: not having enough time to write all the things and meet all the people

Data science contracting

My goal with contracting/consulting is to have a steady income stream while working on my own projects. As my projects are small enough to be done only by me (with optional outsourcing to contractors), this means I have infinite runway to pursue them. While this is probably not the best way of building a Silicon Valley-style startup that is going to make the world a better place, many others have applied this approach to building a so-called lifestyle business, which is what I want to achieve.

Early on, I realised that doing full-on consulting would be too time consuming, as many clients expect full-time availability. In addition, constantly needing to find new clients means that not much time would be left for work on my own projects. What I really wanted was a stable part-time gig. The first one was with GetUp (who reached out to me following a workshop I gave at General Assembly), where I did some work on forecasting engagement and churn. In parallel, I went through the interview process at DuckDuckGo, which included delivering a piece of work to production. DuckDuckGo ended up wanting me to work full-time (like a few other companies), so last month I started a part-time (three days a week) contract at Commonwealth Bank. I joined a team of very strong data scientists – it looks like it’s going to be interesting.

Highlights: seeing my DuckDuckGo work every time I search for a Python package, the work environment at GetUp
Lowlights: chasing leads that never eventuated

Bandcamp Recommender (BCRecommender)

I’ve written a several posts about BCRecommender, my Bandcamp music recommendation project. While I’ve always treated it as a side-project, it’s been useful in learning how to get traction for a product. It now has thousands of monthly users, and is still growing. My goal for BCRecommender has changed from the original one of finding music for myself to growing it enough to be a noticeable source of traffic for Bandcamp, thereby helping artists and fans. Doing it in side-project mode can be a bit challenging at times (because I have so many other things to do and a long list of ideas to make the app better), but I’ve been making gradual progress and discovering a lot of great music in the process.

Highlights: every time someone gives me positive feedback, every time I listen to music I found using BCRecommender
Lowlights: dealing with Parse issues and random errors

Price Dingo

The inability to reliably compare prices for many types of products has been bothering me for a while. Unlike general web search, where the main providers rank results by relevance, most Australian price comparison engines still require merchants to pay to even have their products listed. This creates an obvious bias in the results. To address this bias, I created Price Dingo – a user-centric price comparison engine. It serves users with results they can trust by not requiring merchants to pay to have their products listed. Just like general web search engines, the main ranking factor is relevancy to the user. This relevancy is also achieved by implementing Price Dingo as a network of independent sites, each focused on a specific product category, with the first category being scuba diving gear.

Implementing Price Dingo hasn’t been too hard – the main challenge has been finding the time to do it with all the other stuff I’ve been doing. There are still plenty of improvements to be made to the site, but now the main goal is to get enough traction to make ongoing time investment worthwhile. Judging by the experience of Booko’s founder, there is space in the market for niche price comparison sites and apps, so it is just a matter of execution.

Highlights: being able to finally compare dive gear prices, the joys of integrating Algolia
Lowlights: extracting data from messy websites – I’ve seen some horrible things…

Marine conservation

The first thing I did after leaving my last job was go overseas for five weeks, which included a ten-day visit to Israel (rockets!) and three weeks of conservation diving with New Heaven Dive School in Thailand. Back in Sydney, I joined the Underwater Research Group of NSW, a dive club that’s involved in many marine conservation and research activities, including Reef Life Survey (RLS) and underwater cleanups. With URG, I’ve been diving more than before, and for a change, some of my dives actually do good. I’d love to do this kind of stuff full-time, but there’s a lot less money in getting people to do less stuff (i.e., conservation and sustainability) than in consuming more. The compromise for now is that a portion of Price Dingo’s scuba revenue goes to the Australian Marine Conservation Society, and the plan is to expand this to other charities as more categories are added. Update – May 2015: I decided that this compromise isn’t good enough for me, so I shut down Price Dingo to focus on projects that are more aligned with my values.

Highlights: becoming a certified RLS diver, pretty much every dive
Lowlights: cutting my hand open by falling on rocks on the first day of diving in Thailand

The future

So far, I’m pretty happy with this not-having-a-job-doing-my-own-thing business. According to The 1000 Day Rule, I still have a long way to go until I get the lifestyle I want. It may even take longer than 1000 days given my decision to not work full-time on a single profitable project, together with my tendency to take more time off than I would if I had a “real” job. But the beauty of this path is that there are no investors breathing down my neck or the feeling of mental rot that comes with a full-time job, so there’s really no rush and I can just enjoy the ride.