Santa counting money

Many is not enough: Counting simulations to bootstrap the right way

Previously, I encouraged readers to test different approaches to bootstrapped confidence interval (CI) estimation. Such testing can done by relying on the definition of CIs: Given an infinite number of independent samples from the same population, we expect a ci_level CI to contain the population parameter in exactly ci_level percent of the samples. Therefore, we run “many” simulations (num_simulations), where each simulation generates a random sample from the same population and runs the CI algorithm on the sample. We then look at the observed CI level (i.e., the percentage of CIs that contain the true population parameter), and say that the CI algorithm works as expected if the observed CI level is “not too far” from the requested ci_level.

Keen observers may notice that the language I used to describe the process isn’t accurate enough. How many is “many” simulations? How far is “not too far”?

I made a mistake by not asking and answering these questions before. I decided that num_simulations=1,000 is a reasonable number of simulations, and didn’t consider how this affects the observed CI level. The decision to use num_simulations=1,000 was informed by practical concerns (i.e., wanting the simulations to finish within a reasonable timeframe), while ranges for the observed CI level were determined empirically – by observing the results of the simulations rather than by considering the properties of the problem.

The idea of using simulations to test bootstrapped CIs came from Tim Hesterberg’s What Teachers Should Know about the Bootstrap. The experiments presented in that paper used num_simulations=10,000, but it wasn’t made clear why this number was chosen. This may have been due to space limitations or because this point is obvious to experienced statisticians. Embarrassingly, my approach of using fewer simulations without considering how they affect the observed CIs can be seen as a form of Belief in The Law of Small Numbers.

Fortunately, it’s not hard to move away from belief in the law of small numbers in this case: We can see a set of simulations as sampling from Binomial(n=num_simulations, p=ci_level), where the number of “successes” is the number of simulations where the true population parameter falls in the CI returned by the CI algorithm. We can define our desired level of confidence in the simulation results as the simulation confidence, and use the simulation confidence interval of the binomial distribution to decide on a likely range for the observed CI level.

To make this more concrete, here’s a Python function that gives the observed CI level bounds for different values of num_simulations, given the ci_level and simulation confidence. The output from running this function with the default arguments is plotted below.

import numpy as np
import pandas as pd
import scipy.stats

def get_observed_ci_bounds(all_num_simulations=(10, 100, 500, 1000, 2000, 5000, 10000),
                           ci_level=0.95,
                           simulation_confidence=0.99):
    return pd.DataFrame(
        index=pd.Series(all_num_simulations, name='num_simulations'),
        data=[
            np.array(
                scipy.stats.binom.interval(simulation_confidence, n=num_simulations, p=ci_level)
            ) / num_simulations
            for num_simulations in all_num_simulations
        ],
        columns=['low', 'high']
    ) * 100


>>> print(get_observed_ci_bounds())
num_simulations    low    high
10               70.00  100.00
100              89.00  100.00
500              92.40   97.40
1000             93.10   96.70
2000             93.70   96.20
5000             94.18   95.78
10000            94.43   95.55

Plot of observed CI bounds (ci_level=95%, simulation_confidence=99%)

Therefore, when setting num_simulations to 1,000 (as I did in the experiments I presented previously), we can be 99% confident that the observed CI level of a perfect CI algorithm would be between 93.1% and 96.7% when asked to generate 95% CIs. As shown by the following figure, this doesn’t materially change my previous conclusions: On the dataset from those experiments, the Studentized algorithm delivers satisfactory results, while the Percentile and BCa algorithms are quite far from perfection. And of course, we can now quantify their distance from perfection – the CIs they yield in the best case would be acceptable if we wanted 90% CIs, where we expect the observed CI to be in the 87.5% to 92.4% range (obtained by running the function above with ci_level=0.9). As there are better alternatives, I believe that this is a good enough reason to avoid using the Percentile and BCa algorithms.

Revenue confidence intervals with bounds

Notes: See this notebook for code – use the same environment as the original notebook. The cover photo is by Dima D from Pexels.

Software commodities are eating interesting data science work

The passage of time makes wizards of us all. Today, any dullard can make bells ring across the ocean by tapping out phone numbers, cause inanimate toys to march by barking an order, or activate remote devices by touching a wireless screen. Thomas Edison couldn’t have managed any of this at his peak—and shortly before his time, such powers would have been considered the unique realm of God.

– Rob Reid, After On

Being a data scientist can sometimes feel like a race against software innovations. Every interesting and useful problem is bound to become a software commodity. My story seems to reflect that: From my first steps in sentiment analysis and topic modelling, through building recommender systems while dabbling in Kaggle competitions and deep learning a few years ago, and to my present-day interest in causal inference. What can one do to remain relevant in such an environment? Read this post to find out.

Highlights from my past

When I started my PhD in 2009, the plan was to work on sentiment analysis of opinion polls. This got me into applied machine learning using Java and Weka, with which I made some modest contributions to the field. Today, researching sentiment analysis would feel somewhat pointless, given the plethora of sentiment analysis services. Sentiment analysis is a commodity – using it in practice is a software engineering problem.

Moving forward in my PhD, I got into topic modelling. I learned about Bayesian statistics and conjugate priors. I went through the arduous process of solving integrals by hand and coding a custom Gibbs sampler for the models I specified. Today, I probably wouldn’t bother with the maths. Instead, I’d specify the model and let a probabilistic programming tool like pymc3 or Stan handle the rest. Bayesian inference is now a commodity that’s accessible to any hacker.

A part of my PhD thesis that can probably be replaced by a probabilistic programming tool

Towards the end of my PhD in 2012, I got into Kaggle competitions. Back then, it seemed like “real” data science consisted of building and tuning machine learning models – that’s what Kaggle was all about. While I’ve done quite well in those competitions, I’ve come to realise that the utility of fine-tuning machine learning algorithms is quite limited. In reality, problem definition and solution measurement are more challenging and important. Using machine learning in practice is typically an engineering problem: We can use an existing service or package, follow best practices, and have a great solution for most use cases. No research or custom data work is required beyond turning data into features, which is essentially a data engineering problem. In short, solid machine learning solutions are delivered by solid engineers who glue together solid commodity components. Quoting Google’s Rules of Machine Learning:

To make great products: do machine learning like the great engineer you are, not like the great machine learning expert you aren’t.

Most of the problems you will face are, in fact, engineering problems. Even with all the resources of a great machine learning expert, most of the gains come from great features, not great machine learning algorithms. So, the basic approach is:

  1. Make sure your pipeline is solid end to end.
  2. Start with a reasonable objective.
  3. Add common­-sense features in a simple way.
  4. Make sure that your pipeline stays solid.
Information flow in science and engineering

Many problems in data “science” are actually engineering problems – described best by the flow on the right (source)

Some of my first jobs as a data scientist in industry involved building recommender systems. With recommender systems, much of the work is on the system around the recommendation algorithm. That is, building a recommender system was always mostly an engineering problem. However, these days we have services like AWS Personalize, which does most of the heavy lifting around recommendation. This makes the deployment of recommender systems a pure engineering problem. Like many other problems, recommender systems have been commodified.

I have not done much with deep learning, but there the general trend is even more apparent: Useful innovations quickly turn into tools. Examples include library evolution from Theano to TensorFlow, and commodified prediction services from companies like Google, Amazon, and Microsoft. If you want to use a deep learning service in your application, you probably don’t need a data scientist or even a machine learning engineer. A solid software engineer who can pick the right tools should be enough.

How to remain relevant?

So where does this leave us? It seems to be a more general phenomenon. Essentially every problem that requires specialised knowledge and is valuable ends up attracting repeatable solutions that obviate the need for deep thinking and manual work. These solutions are software commodities. Deploying them is a matter of writing some glue code and fitting them into the overall system – an engineering problem. Implementing data science components to compete with commodities may be interesting and fun, but it’s usually a waste of time when there’s a generic solution that is good enough.

As an individual data scientist, what can you do when your speciality becomes a software commodity? I see a few options:

  1. Embrace the engineering angle. Become good (or better) at engineering solutions. Be pragmatic. Do what what it takes to get the job done. This is probably easier for data scientists like me, who have an engineering background, than for more research/analysis-oriented data scientists. Such data scientists sometimes sneer at engineering work, claiming it’s “fake” data science. Fake or not, solid engineering tools can easily make stubborn data scientists obsolete.
  2. Keep building custom solutions even when viable commodities exist. While this may be more fun for the individual, I believe it isn’t a sustainable approach. The cost of building and maintaining custom solutions will typically be higher than the cost of commodity solutions. Insisting on custom solutions seems like a recipe for becoming irrelevant.
  3. Keep adapting and moving to non-commodity areas. Some things are easier to automate than others. For example, building a machine learning pipeline when the problem is well-defined is relatively easy, but deciding what features to create typically requires some domain expertise. In addition, new research keeps coming out in areas that are less hot than machine learning. One such area is causal inference, where there are still solutions that are yet to be commodified.
  4. Move to the cutting edge. If you want to research novel methods, a “standard” data scientist position may not be for you. Many industry positions are focused on applying proven solutions to a specific organisation. If that doesn’t sound like fun, you’re better off moving to academia or joining a commercial research group.

Are there any other options I don’t see? Let me know in the comments!

A day in the life of a remote data scientist

Earlier this year, I gave a talk titled A Day in the Life of a Remote Data Scientist at the Data Science Sydney meetup. The talk covered similar ground to a post I published on remote data science work, with additional details on my daily schedule and projects, some gifs and Sydney jokes, heckling by the audience, and a Q&A session. I managed to watch it a few months ago without cringing too much, so it’s about time to post it here. The slides are on my GitHub, as is my list of established remote companies, which you may find useful if you want to join the remote work fun.

Revenue confidence intervals

Bootstrapping the right way?

Bootstrapping the right way is a talk I gave earlier this year at the YOW! Data conference in Sydney. You can now watch the video of the talk and have a look through the slides. The content of the talk is similar to a post I published on bootstrapping pitfalls, with some additional simulations.

The main takeaways shared in the talk are:

  • Don’t compare single-sample confidence intervals by eye
  • Use enough resamples (15K?)
  • Use a solid bootstrapping package (e.g., Python ARCH)
  • Use the right bootstrap for the job
  • Consider going parametric Bayesian
  • Test all the things

Testing all the things typically requires writing code, which I did for the talk. You can browse through it in this notebook. The most interesting findings from my tests are summarised by the following figure.

Revenue confidence intervals

The figure shows how the accuracy of confidence interval estimation varies by algorithm, sample size, and the number of bootstrapping resamples on a synthetic revenue dataset. This sort of dataset may occur in freemium scenarios, where several product variations are offered at a few price tiers, including a price of zero (i.e., free). In all cases, the dashed line denotes the requested confidence level of 95%, i.e., the true difference in means between the two revenue distributions should be inside the confidence interval in approximately 95% of the simulations for it to be accurate. Unfortunately, it is clear that both the percentile and BCa algorithms perform poorly on the simulated data. Even with a sample size of 10K, they both yield “95%” confidence intervals that contain the true difference in means less than 90% of the time, i.e., the intervals are too narrow. By contrast, the studentized algorithm gets much closer to the requested confidence level, but this comes at the price of considerably longer runtime due to the need for nested bootstrapping.

Note that the results presented in the talk are slightly different from the figure above. The difference is due to a small bug in the simulation code: I used a constant random seed for all the bootstrapping simulation iterations (every iteration still contained different data). This has led to the surprising finding that accuracy with 10,000 resamples was lower than with 1,000 resamples. I attributed that finding to dataset quirks, and noted that my results may not generalise to all cases. Indeed, I recently ran a similar set of experiments on different data as part of my work at Automattic, and found that the studentized algorithm accuracy wasn’t as impressive as the results shown here.

In addition to synthetic data, the experiments I ran at Automattic included an implementation of an idea by my colleague, Demet Dagdelen: Test accuracy on samples from the full population for a given period (e.g., all sales over a calendar year). In such cases, the full population is well-defined. Therefore, we know the value of the “true” parameters, and we can run the same simulations as on synthetic data. While I can’t share that data, I can say that all algorithms performed much worse on real data than on simulated data. Therefore, we decided to follow the penultimate takeaway and use a parametric Bayesian approach for modelling our data. We may share insights from that line of work on data.blog in the future. In the meantime, comments are very welcome!

How to Increase Retention and Revenue in 1,000 Nontrivial Steps

One of the main projects I worked on last year.

Data for Breakfast

Recently, Automattic created a Marketing Data team to support marketing efforts with dedicated data capabilities. As we got started, one important question loomed for me and my teammate Demet Dagdelen: What should we data scientists do as part of this team?

Even though the term data science has been heavily used in the past few years, its meaning still lacks clarity. My current definition for data science is: “a field that deals with description, prediction, and causal inference from data in a manner that is both domain-independent and domain-aware, with the ultimate goal of supporting decisions.” This is a very broad definition that offers a vague direction for what marketing data scientists should do. Indeed, many ideas for data science work were thrown around when the team was formed. Because Demet and I wanted our work to be proactive and influential, we suggested a long-term marketing data science…

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warning signs

Hackers beware: Bootstrap sampling may be harmful

Bootstrap sampling techniques are very appealing, as they don’t require knowing much about statistics and opaque formulas. Instead, all one needs to do is resample the given data many times, and calculate the desired statistics. Therefore, bootstrapping has been promoted as an easy way of modelling uncertainty to hackers who don’t have much statistical knowledge. For example, the main thesis of the excellent Statistics for Hackers talk by Jake VanderPlas is: “If you can write a for-loop, you can do statistics”. Similar ground was covered by Erik Bernhardsson in The Hacker’s Guide to Uncertainty Estimates, which provides more use cases for bootstrapping (with code examples). However, I’ve learned in the past few weeks that there are quite a few pitfalls in bootstrapping. Much of what I’ve learned is summarised in a paper titled What Teachers Should Know about the Bootstrap: Resampling in the Undergraduate Statistics Curriculum by Tim Hesterberg. I doubt that many hackers would be motivated to read a paper with such a title, so my goal with this post is to make some of my discoveries more accessible to a wider audience. To learn more about the issues raised in this post, it’s worth reading Hesterberg’s paper and other linked resources.

For quick reference, here’s a summary of the advice in this post:

  • Use an accurate method for estimating confidence intervals
  • Use enough resamples – at least 10-15K
  • Don’t compare confidence intervals visually
  • Ensure that the basic assumptions apply to your situation

Pitfall #1: Inaccurate confidence intervals

Confidence intervals are a common way of quantifying the uncertainty in an estimate of a population parameter. The percentile method is one of the simplest bootstrapping approaches for generating confidence intervals. For example, let’s say we have a data sample of size n and we want to estimate a 95% confidence interval for the population mean. We take r bootstrap resamples from the original data sample, where each resample is a sample with replacement of size n. We calculate the mean of each resample and store the means in a sorted array. We then return the 95% confidence interval as the values that fall at the 0.025r and 0.975r indices of the sorted array (i.e., the 2.5% and 97.5% percentiles). The following table shows what the first two resamples may look like for a data sample of size n = 5.

Original sample Resample #1 Resample #2 …
Values 10 30 20 …
12 20 20
20 12 30
30 12 30
45 45 30
— — — —
Mean 23.4 23.8 26 …

The percentile method is nice and simple. Any programmer should be able to easily implement it in their favourite programming language, assuming they can actually program. Unfortunately, this method is just not accurate enough for small sample sizes. Quoting Hesterberg (emphasis mine):

The sample sizes needed for different intervals to satisfy the “reasonably accurate” (off by no more than 10% on each side) criterion are: n ≥ 101 for the bootstrap t, 220 for the skewness-adjusted t statistic, 2,235 for expanded percentile, 2,383 for percentile, 4,815 for ordinary t (which I have rounded up to 5,000 above), 5,063 for t with bootstrap standard errors and something over 8,000 for the reverse percentile method.

In a shorter version of the paper cited above, Hesterberg concludes that:

In practice, implementing some of the more accurate bootstrap methods is difficult (especially those not described here), and people should use a package rather than attempt this themselves.

In short, make sure you’re using an accurate method for estimating confidence intervals when dealing with sample sizes of less than a few thousand values. Using a package is a great idea, but unfortunately I don’t know of any Python bootstrapping package that is feature-complete: ARCH and scikits-bootstrap support advanced confidence interval methods but don’t support analysis of two samples of uneven sizes, while bootstrapped works with samples of uneven sizes but only supports the percentile and the reverse percentile method (which Hesterberg found to be even less accurate). If you know of any better Python packages, please let me know! (I don’t use R, but I suspect the situation is better there). Update: ARCH now supports analysis of samples of uneven sizes following an issue I reported. It seems to be the best Python bootstrapping package, so I recommend using it.

Pitfall #2: Not enough resamples

Accurate bootstrap estimates require a large number of resamples. Many code snippets use 1,000 resamples, probably because it looks like a large number. However, seeming large isn’t enough. Quoting Hesterberg again:

For both the bootstrap and permutation tests, the number of resamples needs to be 15,000 or more, for 95% probability that simulation-based one-sided levels fall within 10% of the true values, for 95% intervals and 5% tests. I recommend r = 10,000 for routine use, and more when accuracy matters.

[…]

We want decisions to depend on the data, not random variation in the Monte Carlo implementation. We used r = 500,000 in the Verizon project.

That’s right, half a million resamples! Accuracy mattered in the Verizon case, as the results of the analysis determined whether large penalties were paid or not. In short, use at least 10-15,000 resamples to be safe. Don’t use 1,000.

Pitfall #3: Comparison of single-sample confidence intervals

Confidence intervals are commonly used to decide if the difference between two samples is statistically significant. Bootstrapping provides a straightforward way of estimating confidence intervals without making assumptions about the way the data was generated. For example, given two samples, we can obtain confidence intervals for the mean of each sample and end up with a plot like this:

Overlapping confidence intervals

When looking at this plot, some people may conclude that the difference between the groups isn’t statistically significant because the confidence intervals overlap. However, overlapping confidence intervals don’t imply a lack of statistical significance because it is possible for the confidence interval of the difference between the sample means to not contain zero. Prasanna Parasurama explained why this happens in this post. While this issue isn’t unique to bootstrapping, it’s worth remembering that when comparing two groups, we need to obtain the confidence interval for the difference in the parameter we’re comparing, not compare single-sample confidence intervals.

For a concrete example, consider a case where we’re looking at a binary outcomes (yes/no or 1/0), which occur in coin flips or online A/B tests. Sample A consists of 2,150 zeroes and 350 ones, while sample B consists of 2,250 zeroes and 440 ones. As these are fairly large samples, we can use the bootstrap percentile method to obtain 95% confidence intervals for the mean of each sample. As the following figure shows, these intervals overlap. If we use the same method to also obtain a 95% confidence interval for the difference in means between B and A, we see that it doesn’t include zero. Therefore, we can say that the difference between B and A is statistically significant, despite the overlap between the single-sample confidence intervals.

Overlapping significance intervals with a statistically significant difference

It’s worth noting that when analysing binary outcomes, we can make stronger assumptions about the data rather than use bootstrapping to obtain confidence intervals. Erik Bernhardsson suggests using the Beta distribution to obtain single-sample confidence intervals, but as we’ve seen, they don’t tell us enough about the differences between samples. I suggested using a Bayesian approach in the past, which makes explicit modelling assumptions that allow us to encode our prior knowledge on the specific environment where the data was generated. For example, when running online A/B tests, we often have a ballpark figure for reasonable results, which can be used in the Bayesian A/B testing calculator I built.

Pitfall #4: Unrepresentative and dependent samples

While the basic bootstrap makes no assumption about the underlying distribution of the data, it is not assumption-free. For example, when dealing with correlated data points from a time series, using the basic bootstrapping approach is wrong because it assumes that the data points are independent. Instead, a block bootstrap should be used – see the ARCH package for some implementation examples. In addition, bootstrapping doesn’t solve problems with the underlying sampling approach. For example, the data sample may not be representative of the population because of its small size, or there may be selection biases and measurement errors. No amount of bootstrapping is going to help with such issues. In general, it always helps to be aware of the data’s generation process, e.g., different considerations apply when dealing with data from online experiments versus observational studies.

Conclusion and next steps

While bootstrapping is a powerful method, its initial impression of simplicity is misleading. To draw valid conclusions, it’s a good idea to use a package and be aware of considerations that are specific to the analysed data sample. However, if you’re already increasing your awareness of the data and its generation process, it may make sense to explicitly encode your assumptions in the model. This is where another hacker resource would come in handy: Probabilistic Programming & Bayesian Methods for Hackers by Cam Davidson-Pilon. Admittedly, it’s a bit longer than the average blog post or conference talk, but it is worth reading.

Going down the bootstrapping rabbit hole has reminded me of an important lesson: Blog posts and talks – especially ones with the word hacker in the title – may be a good starting point, but they shouldn’t be relied on for serious work. Instead, it is better to consult peer-reviewed resources and textbooks, such as the references listed in ARCH’s documentation. In my future explorations of bootstrapping and other methods, I will heed Abraham Lincoln’s timeless advice to not trust everything I read on the internet.

Abraham Lincoln internet quote

Update (Oct 2019): I published a post summarising a talk I gave on the topic, complete with simulation code that illustrates the issues with some bootstrapping algorithms.

Chicken and eggs

The most practical causal inference book I’ve read (is still a draft)

I’ve been interested in the area of causal inference in the past few years. In my opinion it’s more exciting and relevant to everyday life than more hyped data science areas like deep learning. However, I’ve found it hard to apply what I’ve learned about causal inference to my work. Now, I believe I’ve finally found a book with practical techniques that I can use on real problems: Causal Inference by Miguel Hernán and Jamie Robins. It is available for free from their site, but is still in draft mode. This post is a short summary of the reasons why I think Causal Inference is a great practical resource.

One of the things that sets Causal Inference apart from other books on the topic is the background of its authors. Hernán and Robins are both epidemiologists, which means they often have to deal with data with strong limitations on sample size and feasibility of experiments. Decisions driven by causal inference in epidemiology can often make the difference between life and death of individuals. Hence, the book is full of practical examples.

The book focuses on randomised controlled trials and well-defined interventions as the basis of causal inference from both experimental and observational data. As the authors show, even with randomised experiments, the analysis often requires using observational causal inference tools due to factors like selection and measurement biases. Their insistence on well-defined interventions is particularly refreshing, as one of the things that bothers me about the writings of Judea Pearl (a prominent researcher of causal inference) is the vagueness of statements like “smoking causes cancer” and “mud doesn’t cause rain”. The need for well-defined interventions was summarised by Hernán in the article Does water kill? A call for less casual causal inferences.

Unlike some other resources, Causal Inference doesn’t appear to be too dogmatic about the framework used for modelling causality. I’m not an expert on where each idea originated, but it seems like the authors mix elements from the potential outcomes framework and from Pearl’s graphical models. They also don’t neglect time as an important consideration in cause-and-effect relationships. In fact, the third part of the book is dedicated to the topic of time-varying treatments and effects.

The practicality of the book is also demonstrated by the fact that it comes with code examples in multiple languages. In addition, the authors don’t dwell too much on the philosophy of causality. While it is a fascinating topic, the opening paragraphs of the book make its goals clear:

By reading this book you are expressing an interest in learning about causal inference. But, as a human being, you have already mastered the fundamental concepts of causal inference. You certainly know what a causal effect is; you clearly understand the difference between association and causation; and you have used this knowledge constantly throughout your life. In fact, had you not understood these causal concepts, you would have not survived long enough to read this chapter–or even to learn to read. As a toddler you would have jumped right into the swimming pool after observing that those who did so were later able to reach the jam jar. As a teenager, you would have skied down the most dangerous slopes after observing that those who did so were more likely to win the next ski race. As a parent, you would have refused to give antibiotics to your sick child after observing that those children who took their medicines were less likely to be playing in the park the next day.

Since you already understand the definition of causal effect and the difference between association and causation, do not expect to gain deep conceptual insights from this chapter. Rather, the purpose of this chapter is to introduce mathematical notation that formalizes the causal intuition that you already possess. Make sure that you can match your causal intuition with the mathematical notation introduced here. This notation is necessary to precisely define causal concepts, and we will use it throughout the book.

I won’t try to summarise the technical aspects of the book – partly because I don’t fully understand it all, and partly because the book itself is already a summary of a very rich research area. However, I’m likely to go back and reread the book in the future, with the goal of applying the techniques from the book to my work. I’d also like to take Hernán’s causal inference course as a way of practising what I’ve learned from the book. For people who want a non-technical summary of the topics covered by the book, I recommend the article The c-word: Scientific euphemisms do not improve causal inference from observational data. If you’re curious about other (less practical) causality books I’ve read, check out my causal inference reading list and my two previous posts on the topic: Why you should stop worrying about deep learning and deepen your understanding of causality instead and Diving deeper into causality: Pearl, Kleinberg, Hill, and untested assumptions.

Introducing pipe, The Automattic Machine Learning Pipeline

One of the main projects I’ve been working on over the past year.

Data for Breakfast

Screen Shot 2018-11-06 at 14.54.48

A generalized machine learning pipeline, pipe serves the entire company and helps Automatticians seamlessly build and deploy machine learning models to predict the likelihood that a given event may occur, e.g., installing a plugin, purchasing a plan, or churning.

A team effort, pipe provides general, long-term, and robust solutions to common or important problems our product and marketing teams face. When I first joined Automattic almost exactly three years ago, my tasks were two-fold:

  1. I had the autonomy and freedom to delve deep into topics of my choice, which at the time revolved around uncovering the networks hiding withinourcommunitiesusingnetworkscience.
  2. But like most data scientists in the industry, most of my time was spent serving product and marketing teams by providing answers to their data questions which ranged from running simple SQL-like queries to doing more in-depth — but one-off — statistical analyses.

We soon…

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Angels Beach

Reflections on remote data science work

It’s been about a year and a half since I joined Automattic as a remote data scientist. This is the longest I’ve been in one position since finishing my PhD in 2012. This is also the first time I’ve worked full-time with a fully-distributed team. In this post, I briefly discuss some of the top pluses and minuses of remote work, based on my experience so far.

+ Flexible hours
– Potentially boundless work

By far, one of the top perks of remote work with a distributed team is truly flexible hours. I only have one or two synchronous meetings a week, and in the rest of my time I’m free to work the hours I prefer. No one expects me to be online at specific times, as long as the work gets done and I respond to pings within a reasonable time. As I’m a morning person, this means that I typically work a few hours in the early morning, take a long break (e.g., to surf or run some errands), and then work a few more hours in the afternoon or early evening.

The potential downside of such flexibility is not being able to stop working, especially as most of my colleagues are in Europe and North America. I deal with this by avoiding all work communications during my designated non-work hours. For example, I don’t have any work-related apps on my phone, I keep all my work tabs in a separate tab group, and I turn Slack off when I’m not working. I found that this approach sets enough of a boundary between my work and personal life, though I do end up thinking about work problems outside work hours occasionally.

+ More time for non-work activities
– There’s never enough time!

Not commuting freed up the equivalent of a workday in my schedule. In addition, having flexible hours means that I can make time in the middle of the day for leisure activities like surfing and diving. However, it’s still a full-time job, so I’m not completely free to pursue non-work activities. It often feels like there isn’t enough time in the day, as I can always think of more stuff I’d like to do. But my current situation is much better than having to commute on a daily basis. Even though it’s been a relatively short time, I find the idea of going back to full-time office work hard to imagine.

+ No need to attend an office
– Possible isolation from colleagues (and the real world)

Offices – especially open-plan offices – are not great places to get work done. This is definitely the case with work that requires a high level of concentration over uninterrupted blocks of time, like coding and data analysis. Working from home is great for avoiding distractions – there’s no need for silly horse blinders here (though I do enjoy looking at the bird and lizard action outside my window).

One good thing about offices is the physical availability of colleagues. It’s easy to ask others for feedback, socialise over drinks or shared meals, and keep up to date with company politics. Automattic works around the lack of daily physical interaction by running a few meetups a year. The number of people attending a meetup can vary from a handful for team meetups, to hundreds for the annual Grand Meetup. In all cases, the idea is to bring employees together for up to a week at a time to work and socialise. In my experience, the everyday distance creates a craving to attend meetups. I’ve never worked in a place where co-workers were so enthusiastic about spending so much time together – with non-distributed companies, team building is often seen as a chore. I suppose that the physical distance makes us appreciate the opportunity to be together and make the most of this precious time – it’s a bit like being in a long-distance relationship.

That said, in the majority of the time, isolation can be a problem. As I’m based in Australia, I probably feel it more than others – most of my teammates are offline during my work hours, which means that there’s no one to chat with on Slack. This isn’t a huge issue, but I do need to ensure I get enough social interaction through other avenues. As the jobs page of Bandcamp (another distributed company) used to say: “If you do not have a strong social structure outside of work then employment at Bandcamp will likely lead to heart disease and an early death. We’re hiring!”

+ Most communication is written
– Information overload

As Automattic is a fully-distributed company, most of the communication is done in writing. The main tools are Slack and internal forums called P2s (emails are rarely used). This makes catching up on the latest company news easy in comparison to places that rely more heavily on synchronous meetings. The downside of so much written communication is potential information overload. It is impossible to follow all the P2 posts, and even keeping up with stuff I should know can sometimes be overwhelming. I especially feel it in the mornings, as most of my colleagues work while I’m sleeping. Therefore, catching up on everything that happened overnight and responding to pings often takes over an hour – things are rarely as I left them when I last logged off. I experience this same feeling of being overwhelmed when coming back from vacation. Depending on the length of time away, it can take days to catch up. On the plus side, this process doesn’t rely on someone filling me in – it’s all there for me to read.

+ Free trips around the world
– Jet lag and flying

As noted above, Automatticians meet in person a few times a year. Since joining, I attended meetups in Montreal, Whistler, Playa del Carmen, Bali, and Orlando. In some cases, I used the opportunity for personal trips near the meetup locations. Such trips can be a lot of fun. However, the obvious downside when travelling from Australia is that getting to meetups usually involves days of jetlag and long flights (e.g., the 17-hour Dallas to Sydney trip). Nonetheless, I still enjoy the travel opportunities. For example, I doubt I would have ever visited Florida and snorkelled with manatees if it wasn’t for Automattic.

+ Exposure to diverse opinions and people
– Cultural differences can pose challenges

Australia’s population is made up of many migrants, especially in the tech industry. However, all such migrants have some familiarity with Australian culture and values. The composition of Automattic’s workforce is even more diverse, and it lacks the unifying factor of everyone choosing to live in the same place. This is mostly positive, as I find the exposure to a diverse set of people interesting, and everyone tends to be friendly, welcoming, and focused on the work rather than on cultural differences. However, it’s important to be aware of differences in communication styles. There’s also a wider range of cultural sensitivities than when working with a more homogeneous group. Still, I haven’t found it to be much of an issue, possibly because I’m already used to being a migrant. For example, moving to Australia from Israel required some adjustment of my communication style to be less direct.

Closing words

Overall, I like working with Automattic. For me, the positives outweigh the negatives, as evidenced by the fact that it’s the longest I’ve been in one position since 2012. Doing remote data science work doesn’t seem particularly different to doing any other sort of non-physical work remotely. I hope that more companies will join Automattic and the growing list of remote companies, and offer their employees the option to work from wherever they’re most productive.

Update (March 2019): I also covered similar topics in a Data Science Sydney talk about a day in the life of a remote data scientist.

What would you say you do here?

Defining data science in 2018

I got my first data science job in 2012, the year Harvard Business Review announced data scientist to be the sexiest job of the 21st century. Two years later, I published a post on my then-favourite definition of data science, as the intersection between software engineering and statistics. Unfortunately, that definition became somewhat irrelevant as more and more people jumped on the data science bandwagon – possibly to the point of making data scientist useless as a job title. However, I still call myself a data scientist. Even better – I still get paid for being a data scientist. But what does it mean? What do I actually do here? This article is a short summary of my understanding of the definition of data science in 2018.

It’s not all about machine learning

As I was wrapping up my PhD in 2012, I started thinking about my next steps. I knew I wanted to get back to working in the tech industry, ideally with a small startup. But it wasn’t clear to me how to market myself – my LinkedIn title at the time was “software engineer with a research background”, which is a bit of a mouthful. Around that time I heard about Kaggle and decided to try competing. This went pretty well, and exposed me to the data science community globally and in Melbourne, where I was living at the time. That’s how I first met Adam Neumann, the founder of Giveable, a startup that aimed to recommend gifts based on social networking data. Upon graduating, I joined Giveable as a data scientist. Changing my LinkedIn title quickly led to many other offers, but I was happy to be working on Giveable – I felt fortunate to have found a startup job that was related to my PhD research on recommender systems.

My understanding of data science at the time was heavily influenced by Kaggle and the tech industry. Kaggle was only about predictive modelling competitions back then, and so I believed that data science is about using machine learning to build models and deploy them as part of various applications. I was very comfortable with that definition, having spent my PhD years on several predictive modelling tasks, and having worked as a software engineer prior to that.

Things have changed considerably since 2012. It is now much easier to deploy machine learning models, even without a deep understanding of how they work. Many more people call themselves data scientists, including some who are more focused on data analysis than on building data products. Even Kaggle – which is now owned by Google – has broadened its scope beyond modelling competitions to support other types of analysis. Numerous articles have been published on the meaning of data science in the past six years. We seem to be going towards a broad definition of the field, which includes any type of general data analysis. This trend of broadening the definition may make data scientist somewhat useless as a job title. However, I believe that data science tasks remain useful, as shown by the following definitions.

Recent definitions by Hernán, Hawkins, and Dubossarsky

In a recent article, Hernán et al. classify data science tasks into three types: description, prediction, and causal inference. Like other authors, they argue that causal inference has been neglected by traditional statistics and some scientific disciplines. They claim that the emergence of data science is an opportunity to get causal inference “right”. Further, they emphasise the importance of domain expert knowledge, which is essential in causal inference. Defining data science in this broad manner seems to capture the essence of what the field is about these days. However, purely descriptive tasks are still often performed by data analysts rather than scientists. And the distinction between prediction and causal inference can be a bit fuzzy, especially as the tools for the latter are at a lower level of maturity. In addition, while I agree with Hernán et al. that domain expertise is important, it seems unlikely that this will forever be the case. No one is born an expert – expertise is gained by learning from and interacting with the world. Therefore, it’s plausible that gaining expertise can and will be automated. Further, there are numerous cases where experts were proven to be wrong. For example, it wasn’t so long ago that doctors recommended smoking.

Despite the importance of domain knowledge, one can argue that scientists that specialise in a single domain are not data scientists. In fact, the ability to go beyond one domain and think of data in a more abstract manner is what makes a data scientist. Applying this abstract knowledge often requires some domain expertise or input from domain experts, but most data science techniques are not domain-specific – they can be applied to many different problems. John Hawkins explains this point well in an article titled why all scientists are not data scientists:

Those scientists and statisticians who have focused themselves on understanding the limitations and possibilities of making inferences from experimental data are the ones who are the forerunners to data scientists. They have a skill which transcends the particulars of what it takes to do lab work on cell cultures, or field studies for ecology etc. Their core skill involves thinking about the data involved at an abstracted level. To ask the question “given data with these properties, what conclusions can we draw?”

Finally, according to Eugene Dubossarsky, “there’s only one purpose to data science, and that is to support decisions. And more specifically, to make better decisions. That should be something no one can argue with.” This goal-focused definition is unsurprising, given the fact that Eugene runs a training and consulting business and has been working in the field for over 20 years. I’m not going to argue with him, but to put it all together, we can define data science as a field that deals with description, prediction, and causal inference from data in a manner that is both domain-independent and domain-aware, with the ultimate goal of supporting decisions.

What about AI?

Everyone loves a good buzzword, and these days AI (Artificial Intelligence) is one of the hottest buzzwords. However, despite what some people may try to tell you, AI is unlikely to make data science obsolete any time soon. Following the above definition, as long as there is a need to make decisions based on data, there will be a need for data scientists. This includes decisions that aren’t made by humans, as data scientists are involved in building systems that make decisions autonomously.

The resurgence of AI feels somewhat amusing given my personal experience. One of the reasons I decided to pursue a PhD in natural language processing and personalisation was my interest in what I considered to be AI back in 2008. My initial introduction to the field was through an AI course and a project I did as part of my bachelor’s degree in computer science. However, by the time I graduated from my PhD, saying that I’m an AI expert seemed less useful than calling myself a data scientist. It may be that the field is about to shift again, and that rebranding as an AI expert would be more beneficial (though I’d be doing exactly the same work). Titles are somewhat silly – I’m going to continue working with data to support decisions for as long as there is demand for this kind of work and I continue enjoying it. There is plenty to learn and develop in this area, regardless of buzzwords and sexy titles.