As the time I’ve spent in my job is nearing two years, I want to reflect on how it’s been and how I’ve changed.
I went into this job full of excitement, anxiousness and with some sadness. I was excited to get to know this firm, the world of business and to apply what I’d learned. The excitement mixed with anxiousness as I pondered what projects I would work on, how my colleagues would be and how I’d adjust. I was also sad, because I didn’t feel completely ready to leave academia. I liked it there and leaving felt like admitting failure.
My best memories over the last two years come from having a good time with people, often in some distant place. My worst memories are from when relationships in the team were broken and I felt like I worked too much with too little effect.
While I’ve learned some new technical skills, I found it more important to improve my general way of working and communicating. I needed to get the balance right between being thorough and being pragmatic, between questioning assumptions and accepting guidance from others. I learned a new vocabulary to speak about my work and became more at ease with sharing unfinished work products to ask for early feedback. I learned to better synthesize large amounts of information and how to mine my pool of prior experiences to propose the next step in an analysis. Most of all, I needed to accept criticisms and to work on my faults.
I’m thankful of how varied the projects I worked on have been. My proudest moments happened in short, intense work episodes during which I had a strong sense of effectiveness: That I was the right person at the right place and my work made a difference.
I’m still the same person I was when I went into this job. My basic opinions, preferences and relative strengths hardly changed. I only realize what I’ve learned when I meet somebody who doesn’t know something, like a new starter who makes the same mistakes I made before.
To keep balance, it’s been important for me to keep in touch with people outside my job and to stay focused on what I want in the long run. Incentives in the business world can let you lose sight of the boundary between what’s investment and what’s consumption. You collect airline miles and hotel points, go to trainings in nice places and eat dinners in fancy restaurants. But consumption is not durable and while expectations rise, memories fade.
Two years in, the feelings of excitement, anxiousness and sadness are gone.
My work has become more routine, as I find myself in situations I’ve been in before. The uncertainties about my job are less and I find it easier to start into new projects and know what to expect. A residue of uncertainty has stayed, as I’ll never know what the next project will bring or if I’ll like being around the people I work with.
The sadness about leaving academia has fallen with every month since I stepped out. About half a year ago it crossed the threshold where I would find it hard to imagine going back. I like that work outside academia is faster and has direct effects. It’s less thorough, but that’s ok. I realized that many of the parts I enjoyed in academia weren’t specific to it, such as exploring new topics, analyzing data or presenting results.
What’s next I don’t know. I enjoy my job and have no plans for a change. But I know that the nagging feeling of needing a change will return and I won’t fight it when it does.
When thinking of successful startup founders, many people have Mark Zuckerberg, Steve Jobs or Bill Gates in mind - in general a young college-dropout working from a garage in Silicon Valley.
In a new paper (pdf) Pierre Azoulay, Benjamin F. Jones, J. Daniel Kim and Javier Miranda attack this misconception of young founders being more successful than older ones.
The authors take comprehensive census data for the United States and show that the average age for successful founders is in the 40-45 year range. As they write:
The mean age at founding for the 1-in-1,000 fastest growing new ventures is 45.0.
They show in clever ways that this unexpectedly high age average also holds for high-growth startups and those in the tech industry, by using ex ante and ex post measures of growth and tech specialization.
Ex ante, they measure the industry a founder is in, whether the firm received venture capital (VC) funding and whether the firm holds a patent. Ex post, they are able to measure employment and sales growth for these firms 3, 5 and 7 years after founding and they measure which firms “exit successfully” (meaning they are sold or go public). The average ages are similar for all these different subgroups.
Azoulay et al. also show that the results are not just driven by the fact that middle-aged founders are more common thus pushing up the age average. Instead they show that middle-aged founders are more likely to succeed.
One reason for this might be the importance of prior experience. They show that the probability of success (being one of the 1-in-1,000 fastest growing firms) doubles after three years of work in the relevant industry.
The authors also discuss extreme outliers (Zuckerberg etc.) and provide the following verdict:
With [keeping the anecdotal type of evidence] in mind, however, the patterns may suggest a potential reconciliation between the existence of great young entrepreneurs and the advantages of middle age. Namely, extremely talented people may also be extremely talented when young. These individuals may succeed at very young ages, even when people (including these young successes) get better with age. Thus there is no fundamental tension between the existence of great young entrepreneurs and a general tendency for founders to reach their peak entrepreneurial potential later in life.
Time period: My biggest criticism is that startups only make it into the sample used for the most important analyses if they were founded in 2007-2009. This is a short sample to start with, but more importantly this was also the time of the Financial Crisis.
During this large recession, investors might have stopped funding and banks might have stopped giving credit first to the more speculative and riskier firms run by younger people. This might bias the results to older founders. Table A4 shows averages separately for the different years, but all these years are the aftermath of the Financial Crisis, so that doesn’t address this problem.
You could also think that there might be some cohort effect of all the 40-year olds in those years having been young in the 90s when they might have started their first companies during the first Dotcom gold rush.
Measures of success: A second criticism I have is that this paper defines employee and revenue growth as the key measures of a startup’s success. The most clean measure would is a startup’s value, which is a more complete summary of a firm’s ability to generate profits in the future. This is difficult to measure even for established firms, so I understand why the authors did not do this.
However, this can lead to extreme examples where the author’s measures make less sense. Take the example of WhatsApp: It had only 50 employees in early 2013, but was acquired by Facebook a year later for 19 billion USD. (To be fair, with 400 million users in late 2013 and each paying a dollar for the product a year, revenues would already have been impressive).
On balance: But in total, I think this is an excellent paper. It hits a sweet spot of 1) discussing a relevant topic, 2) bringing novel results that shift my views and 3) being well-executed and thus trust-worthy.
I also find it somewhat comforting to think that you don’t have to rush into entrepreneurship with a half-baked idea, but that there are advantages to starting a firm later in life.
Playing with the data
Most of the data is confidential, so the authors can’t share it. But they’ve also collected a list of companies named by media and VC organizations that were considered particularly promising and the age of their founders. This data we can play with.
In her memoir “Uncanny Valley”, Anna Wiener tells the story of how she went from a job in the NY publishing industry to work for Silicon Valley startups.
Wiener describes her years working in different startups and in doing so chronicles the vibe and the good and bad aspects of tech culture in San Francisco. She then quit her job and is now a writer for The New Yorker where she still covers the tech sector.
The last tech company Wiener worked for is a large company providing a site for collaborating on code. She doesn’t name it, but it is without doubt Github.
There, she learns about remote working culture. I read this book at the beginning of March this year, when Corona less acutely affected my life and work than it does now. Then I found the contrast between work life at Github and at what experienced at different companies very pronounced.
This is Wiener on Github’s remote working culture:
Everyone was encouraged to work how, where, and when they worked best—whether that meant three in the morning in the San Francisco office, referred to as HQ, or from inside a hammock on Oahu.
To ensure that all employees were on equal footing regardless of geography, the majority of business was conducted in text. This was primarily done using a private version of the open-source platform, as if the company itself were a codebase. People obsessively documented their work, meetings, and decision-making processes. All internal communications and projects were visible across the organization. Due to the nature of the product, every version of every file was preserved. The entire company could practically be reverse engineered.
Our remote coworkers had wants. They often spoke of feeling like second-class citizens. As the company became more corporate, the culture had gone from remote-first to remote-friendly.
As the Coronavirus forces me too to work from my home, some of those aspects start to feel familiar: I find chat programs more useful than before. For me they’re not about documenting my work, but about asking small questions that I would ask someone directly when I’m in a room with them, but that I might not call them for.
There are also differences: The tech sector is more laid back than other sectors and so far we haven’t started taking calls in our bed or a hammock, even for internal calls.
My reading this year fell mainly into three buckets: Memoirs, novels by Sally Rooney and books by the historian Thomas Madden.
These were my favorites:
“Maoism: A Global History”, by Julia Lovell. Explains how maoist thought and actions by the Chinese government influenced movements in other countries. Not much talked today - especially by the Chinese government.
“Aladdin: A New Translation”, by Yasmine Seale. This is the first documented Aladdin story, intriguingly first published in France in the early 18th century. I wonder about the morality of it: If you had some tool (a Jinn in this tale) that could get you whatever you want, I don’t think the acceptable way to behave would be to kill a magician, get the biggest house full of diamonds and force the terrified princess to sleep in your bed.
“Writing My Wrongs”, by Shaka Senghor. He describes life on the streets of Detroit, the murder he committed, 19 years in prison of which he spent 7 years in an isolation cell (4 years in a row).
“The Education of an Idealist”, by Samantha Power. I liked the start of the book more than the part when she became ambassador. I think the legacy of her administration’s foreign policy doesn’t hold up well and I didn’t learn much from her time at the UN. They seemed to spend a lot of time on symbolic gestures, but got the big things on Syria wrong. I much prefered her very personal story of her long path through war journalism, academia and government.
“Conversations with Friends”, by Sally Rooney. There are lead characters that I would have guessed to relate better to than a female bisexual Irish poet, but this is a book I knew from the first page I would enjoy and read to the end.
“Shoe Dog: A Memoir by the Creator of Nike”, by Phil Knight. Fascinating story of the founding of Nike, touching on how the early running subculture and post-war Japan. Knight is a different founder from those we have in our minds today: He first finished his MBA and worked in accounting when he needed funds for his business. He had very supportive parents and also turned to them at the beginning for money. (Olli’s review)
Samantha Power is the former US Ambassador to the UN under Obama, a Harvard professor and a former war journalist reporting from 90s Bosnia. In her recent memoir, “The Education of an Idealist”, she describes her upbringing in Dublin with an alcoholic father, migrating to the US and her path through journalism, academia and government.
I like her explanation of the different modes of working in journalism/academia vs. government.
In her early twenties, Power worked as a journalist in wartime Bosnia. She reported from Sarajevo while it was under siege and published many articles in large newspapers and magazines. She learned to write in an engaging way which helped her later, when she became an ambassador to bring the stories of individuals to her official UN meetings, to make other diplomates relate to them and their suffering.
She enjoyed her independence, the travel and the attention and status from seeing her name published in the New York Times or the Washington Post.
But she missed the opportunity of shaping events herself. When she interviewed politicians, she would often think: “I would rather be on the other side of the table.”
So she decided to join law school:
Jonathan Moore, the former US diplomat and refugee expert I met working at Carnegie, had become someone I turned to at critical moments. With school beginning at the start of September, I needed to make a final decision, so I telephoned him and asked what I should do. Jonathan didn’t hesitate. “Get the hell out of there,” he urged me. “You need to break out of the compulsion for power, glory, ego, relevance, contribution. Get out. Get out before it gets you, and you forget what got you in.” I didn’t think self-consciously about power, glory, and ego, but Jonathan knew I didn’t mind seeing my name in print.
Power learned about Obama, the new Senator from Illinois, from his DNC speech, reached out to him through friends and joined his team. She entered his administration to work at the National Security Council in a role focused on managing contacts with the UN and keeping an eye on human rights issues.
But she was frustrated that colleagues would not listen to her, that she could not travel as she liked and that her initiatives would come to nothing. She felt like she wasted her time when she and her colleagues would spend a long time marking up each other’s word documents. She needed to learn the vocabulary, such as that stepping over a level in the hierarchy was a “process foul”.
Time also became more precious:
The aspect of government that I had least appreciated before I joined was the importance—and shortage—of “bandwidth.” So much was going on in the world on any given day that one could easily lose an afternoon editing language in various press releases. Mort, my longtime mentor, urged me to prioritize, helping me understand my days as analogous to my mother’s when she worked in the emergency room.
Suddenly she was in the group making the decisions:
[A mentor] often dispensed wisdom on how government worked, and told me I should not have waited until a high-level meeting had ended to make my point. “Listen,” he said firmly. “If you hear nothing else, hear this. You work at the White House. There is no other room where a bunch of really smart people of sound judgment are getting together and figuring out what to do. It will be the scariest moment of your life when you fully internalize this: There is no other meeting. You’re in the meeting. You are the meeting. If you have a concern, raise it.”
In her words, she “did not yet have the relationships, the clout, or the mastery of bureaucratic processes [she] needed to maximize [her] impact.”
Putting it together
It took her some time and the advice from more experienced colleagues such as Susan Rice to get up to speed and learn to deal with her frustations.
To become effective, she used three strategies:
Keep an eye on the big picture: Don’t forget the long run even though short-run attention is crowded out.
Build a network: She created a weekly “Wine & Cheese” women group to share stories and get advice. And she also played basketball which helped her to get to know the male colleagues in the White House better.
Work on smaller projects: These don’t get top leadership attention and are easier to have an impact on.
I enjoyed reading her story and can relate to it. Working in hierarchies is something you have to learn. I found life as a researcher more free, less structured and more bottom-up. I was expected to search for my own research ideas and I could collaborate freely.
It’s quite different for me as a consultant. Our teams work embedded at the client in large hierarchies, so there are more stakeholder to inform and align with. The target audience usually has limited “bandwith”, as your project is only one of many that concerns them. That’s why it’s important to understand the process and to be concise and accurate in your communication.
While the first mode of working is more conducive for being creative, I now realize I could sometimes have been more effective in academia using some simple strategies. A simple, “end of month” email with what I did the last month and what I planned to do in the next would have gone a long way of keeping my supervisors in the loop, even when we couldn’t meet face to face. It would have provided them with an earlier opportunity to provide guidance, voice concerns or to keep me from starting down some rabbit whole.
In other areas, I think researchers use the same tools successfully already. Most writing guides for papers or presentations already advise to be “top down” - to give the message first and the details later.
In the end, the two modes of working are very different, but I think it’s insightful to change the between the two.
I just came back from the useR! conference in Toulouse which I enjoyed attending and which I recommend.
The conference was good mix of lectures, coding workshops and short presentations of new packages and papers.
The tidyverse has captured this community. Most code examples use the pipe and dplyr’s functions like select() or mutate() without explanation.
The vibe is closer to what I’m used to from academic conferences and it was less crowded and exuberant than NeurIPS.
I mainly visited talks on time series statistics, movement data and a few others.
Time series statistics
The tidyverts collection of packages for tidy time series analysis is fantastic and should be up on CRAN soon. It contains:
tsibble: An evolution of the tibble for time series purposes (itself an update to the dataframe). The tsibble makes it explicit which variable indexes time (e.g. “year”) and which groups the rows (e.g. “country”) and stores the information which frequency the data is running on.
feasts: New plotting tools tailored to the specific frequencies (e.g. seasons or weekdays) and decompositions of series into cycle and trends using different methods (STL, X11, …).
fable: Time series forecasting with many common methods and new visualization functionalities.
Other time series contributions:
A presentation on using random forests with time series data (paper, presentation). There was a lively discussion on how to create block bootstrap samples (overlapping? Moving windows? Size of blocks?) needed for the forest. The solution from the talk was to use a validation sample on which to test for the optimal block size.
For economists, timeseriesdb - developed at KOF Zurich - might be of interest. It provides a good way to store different vintages of time series and their metadata in a database.
The imputeTS package was presented with an application on sensor data. I could well imagine to use that package in a manufacturing analytics study, such as predictive maintenance. In these cases, you often have high-frequency measurements (such as by minute) for thousands of sensors, but the quality of measurements is hard to judge, outliers are common and sometimes missing data is even implicit (such as returning the last value again and again). The presenter pointed out that in these cases, the missingness is often correlated across variables (e.g. when there’s a factory shutdown, bad weather stopping data transmission, etc).
Anomaly detection got good coverage: The package anomaly (presentation), the stray package and a trivago real-world example with an interesting conclusion (presentation).
Movement data is currently my favourite kind of data. It’s spreads across time and space, every data point is “weighty”, describing the repositioning of a giant machine or a group of people and you can use interesting methods to analyze such data.
I was therefore happy to see that there is a lot of work going on in creating packages for drawing maps and analyses of movement data. Presentations: 1, 2, 3, 4. The sf package was presented in two workshops (1, 2).
However, I’ve often found this topic quite difficult to start out with in R and I don’t think it’s become much easier yet. I’m still not convinced that I would go this route if I just needed to draw a quick map. A tool like Tableau takes care of all the underlying stuff such as guessing correctly that some column in your data describes US zip codes and draws the right map based on that.
Other: Packages building, data cleaning, big files
Jenny Bryan held a good tutorial on package development and she made her point really well that we should be writing packages much more often.
Hadley Wickham explained how the great tidyr package gets a facelift, renaming spread() to the more expressive pivot_wider() and gather() to pivot_longer.
I was quite impressed by the disk.frame package. It allows splitting a too-large-for-memory dataset into smaller chungs on the local machine and it only pulls in the columns you need. It also allows for quick staggered aggregations, such as calculating the sum of a variable for the different chunks and then taking the sum of that. Interestingly, that wouldn’t work for other functions such as the median.
I left academia for the private sector half a year ago. I receive many questions about how that’s been, so I hope a blog post might answer most of the questions in one place.
What I do
I handed in my dissertation in the summer and started working as an analytics consultant at a large international consultancy. We are management consultants, traveling to clients and working on projects that range from weeks to months. We bring quantitative firepower to teams and work on projects such as quantitative marketing, optimizing route networks, health care analytics, fraud detection or predictive maintenance. We use many different techniques, such as random forests, time series forecasting, optimization, webscraping or deep learning.
Our studies are often full analytics studies from the start, and then using optimization or ML is more fundamental than “sprinkles on the top”. But it’s true that most of our time and many of our highest-value activities are “simple arithmetic”, such as collecting the right data, cleaning and merging datasets, calculating KPIs or writing a dashboard. I also spend a lot of time on tasks such as writing presentations, maintaining Excel sheets and calls and meetings with clients.
Why I switched
I decided to give the private sector a try for several reasons:
I really like coding. I’d like to get better at it and it’s something that might be valued more in industry than in academia.
I want to apply data science in a real-world setting (not least to find out if its all just a hype).
I found research very satisfying, but I don’t want to specialize on one topic. My dissertation touched many areas and the demands of academia would likely have made it necessary for me to be more focused.
It’s a cliché, but the thing I like most about my job are the people. My colleagues are inspiring, fun and highly motivated.
People are very friendly and sociable in the business world and consulting attracts people with good “verbal and social” skills.
You can make fast progress when teams work well together. And it’s not as bad to sit in a team room at 1 am if you’re in it together.
3. Speed of projects
I like that projects move faster and at some point they are actually done and you move on to the next one. This has a diversification effect, so even if a project is bad, it’ll be over not too far in the future.
4. Diversity of projects
The variance between projects is huge, because they can differ along so many dimensions: Industry, function, colleagues, place.
For me, it’s a good way to learn about the data science landscape and to see where my skills might be most valuable.
5. Being challenged
In academia you pick your own subject, methods and execute on your own speed. But being thrown into new topics is also activating and doesn’t let you get complacent.
Consulting encourages a strong feedback culture. It’s common to have feedback talks every two weeks. This is very helpful: It’s hard to judge on your own how you’re doing and it means there won’t be any surprises after the project is done.
And yes, you’re better paid.
Advantages of academia
In academia, I had the freedom to pursue my own projects and that’s something I miss in the private sector. Projects are initiated at much higher levels in the hierarchy and in junior roles you’re executing them.
In contrast, economics research is not very hierarchical. You are free to pick your topics and you pitch your ideas to anyone.
At universities, the outputs of your work are public. Almost everywhere else – and especially in consulting – you might have accomplished cool things, but few people will know about it.
Academia is much more like running your own startup. You bear the risks, but you also reap the benefits. The downside is that you’ll have to live with the existential fears: “Will I publish my paper well?”, “Will I get tenure?”, …
3. Following rabbit holes
Management consultants live and breathe opportunity costs. This keeps you on your toes to keep making progress towards the target.
But this absence of slack means that what you learn and what you code is determined by what your project needs right now. You don’t just play detective and work on whatever interests you. In academia, new knowledge is the goal and you have to try many ideas and projects to find what has potential. Most private sector jobs are not “unicorn jobs”.
4. Work hours
I don’t think that I worked much less in academia. But work vs. leisure is much more sharply separated in the private sector.
When and how you work is largely out of your control in consulting and that’s a downside. In research, you have self-determined work hours. You might think it’s an advantage to push hard during the week and then to have the weekend off, but - at least for me - that’s not true.
It’s maybe a bit exaggerated, but I would say that the private sector is more conducive to happiness, but research is a better way to find meaning (see here) in life.
How to apply
As economists, we’re very well prepared for data science jobs. We know econometrics, statistics, mathematics and we have internalized most MBA knowledge (e.g. accounting, corporate finance, marketing). We know how to manage long projects, work independently and be convincing in communicating our results.
However, we’re not obvious hires for data science positions. Many people conflate economics students with business majors and might not consider us quantitative enough. It’s not clear to employers that we know ML or that we can program.
I would recommend to learn R or Python. Nobody uses Stata or Matlab (or Julia, EViews, …). You should know one of the two languages reasonably well to be able to start coding on unfamiliar subjects quickly.
You’ll be expected to learn other languages or software soon, too. For example, I’ve learned VBA, Alteryx and Tableau since I started and none of that I’d used before. But I think it’s important to know R or Python, so that you’re productive from the start and that you can show that you can actually code.
On the ML side, the first step is to actually learn about it. An economics program doesn’t teach you machine learning, so you need to look elsewhere. I recommend online courses, checking for courses at your own computer science department or trying Kaggle competitions. A great way to learn about it and to show that you know it is to apply ML in your research.
Given that the outputs of our academic work are public and that you might have time to pursue side projects, it’s a good idea to create public artifacts. It makes sense to take this into account early in your PhD when choosing research projects. I wrote papers with text mining and webscraping, partly because I wanted to learn about these topics. Having written a paper like that is great for your application, because it’s a public output that proves that you know the subject. I’ve explained my patent paper in most job interviews and it was very helpful.
Because outputs in the private sector are not public, the next best proxy is your work experience and years in your PhD often don’t count. Public artifacts are marks of your skill that can compensate for lack of work experience.
When deciding where to apply, it’s important to consider the framework in which a firm operates. So if you work for a consultancy, then you’re a consultant first and a data scientist second.
I’ve enjoyed the whole experience and I’m happy with my choice. But there are also things I miss about academia.
I attended the NeurIPS (recently renamed from NIPS) conference this year and here are some observations:
I find it remarkable how for many people machine learning is equivalent to deep learning and deep learning is equivalent to computer vision.
Susan Athey talked about causal inference. She’s been doing a lot of work combining econometrics with machine learning, but it was very interesting to see this presented from the other side of the fence. When she asked who knew about instrumental variables, a reasonable number of hands (maybe 5% of a very large crowd) went up.
Edward Felten (Princeton) talked about how experts should talk to decision makers. Basically, don’t come with the “truth” and don’t provide “just the facts”. Structure the decision problem by inquiring about the decision makers preferences and provide information and an option menu of recommendations based on that.
Many of the sessions touched on economics: There were papers on market design, value function iteration or - more close to home - on the social effects of automation.
Differences to economics conferences:
Many more consumers than producers of research
Different demographics: Younger, more nerdy, fewer women, more Asian
Fewer parallel sessions, instead “tutorials” (really lectures) by famous people that last 1.5 hours
Videos/GIFs on slides. Works suprisingly well and engages people. Examples include simulations of how fruit flies move, 3d shapes circling around, bubbling formations of networks
The anthropomorphizing is a bit weird: They say “The algorithm should be able to reason about its own uncertainty”, instead of “We need accurate standard errors”
The money involved is different. We don’t have huge recruiting parties by Uber or have Yo-Yo Ma lead a session on the intersection of music and deep learning.
Montreal is the way I wish France was. People are laid back and cosmopolitan. Everything is in French and people switch between languages without effort.
Keeping up the tradition of the last two years, I’m again providing a list of 10 my favorite books this year.
In between finishing my dissertation, starting a new job and moving to a new city, I read less this year. I was worried that I would have almost no time for reading in my new job, but that hasn’t turned out to be the case. The weekend, late nights in hotel rooms and downtimes while traveling still provide many moments to read.
So here are the books I liked most this year (but which were not necessary published this year) in reverse order:
“Steve Jobs”, by Walter Isaacson. It’s incredible how intimately Isaacson is covering his life. I also enjoy it as a history of the 90s and 00s, a time I lived through.
“The Story of Art”, by E.H. Gombrich. I love this book. This is how to write about art. (Here, too, get the print version.)
“The Internationalists”, by Oona Hathaway and Scott Shapiro. This builds on years of scholarship, but the authors explain concepts in an accessible manner. Which other book would discuss the ideas of Grotius, Carlo Schmitt and Sayidd Qutb? This is a book that has changed how I view the world. A piece of their conclusion:
The example of the Internationalists offers a hopeful message: If law shapes real power, and ideas shape the law, then we control our fate. We can choose to recognize certain actions and not others. We can cooperate with those who follow the rules and outcast those who do not. And when the rules no longer work, we can change them.