Your resume is your cover photo

During your transition from academics to Data Analytics and Data Science you will need to craft a resume. This is a relatively daunting task! And how you present yourself really matters.

First off, what is the goal of your resume? The goal is to generate interest in you. That is to say, your resume is an advertisement. A cover photo of you for potential employers. Its job is to convey the right information in the right way to get you to the next step in the interview process. It’s unlikely that your resume will land you a job on its own. In order to get a job, you will eventually have to show up in person and present yourself at an interview (But that’s a topic for later!). All this to say, the presentation of the information is as important as the information itself. So much so, that I will just talk about presentation for this article.

There are many well-meaning, but unhelpful, resume reviewers in the world. These people may or may not be beneficial to your journey.  I have seen resumes crafted by masters students which look as though they were written by a high school student because a resume/career center coached the applicant into including no details, lots of space, and vacuous technical statements.  I have also seen resumes which are actually abbreviated CVs and are over crowded with unnecessary information.  So, be a little wary of your enthusiastic parents from other fields and friendly school career centers who are mostly focused on non-technical fields. They may guide you towards presentation which does not deliver your desired message. For reviewers, I strongly encourage finding someone within the field you are aiming for.

The next step is to look at as many resumes as you can get your hands on!  Ask your friends, older peers, and classmates. Glance around at job fairs and see what the other resumes look like. It’s important to know what recruiters are seeing so you can understand what the local landscape of resumes looks like for your area.

Presentation Matters! For my field, analytics and data science in retail, we care a great deal about communication skills. Our analysts and scientists are required to communicate with non-technical business partners on a daily basis. So, I often consider the design of someone’s resume as a direct reflection on their ability to present information in a compelling way. Can the applicant present the information she knows best (herself!) in a compelling way? Does she have a clear formatting method that highlights her data visualization or effective communication skills?

A notable presentation pitfall I experienced recently: Does your resume look identical to your colleague’s? I was once handed 4 resumes during a recruitment fair which looked almost identical. In fact, along with identical presentation, the content was similar as well. The candidates went to the same school and many worked at the same company! So, despite being well laid out and having good material, I had no good way to distinguish between candidates. It became and accidental game of “who wore it best?”. And I promise, that is not a game you want to be playing with your resume.

A second common pitfall is length.  If you have a PhD or 10+ years of experience after undergraduate, then I’m expecting a 2 page resume. It’s not mandatory, but if I don’t see all the information I need on the first page and there is no second page, then I’ll get nervous about your ability to determine what’s important to share. The best stuff should be on page 1, but if I like what I see there, then I’m hoping to turn the page over and learn more about your community engagement within your field, leadership skills and, perhaps, accolades/publications.

The third risk is to fall too far towards presentation. If your resume looks too far outside the norm for the field, then you may be met with unconscious bias from the hiring manager. That bias might be good or bad, so, just be aware of when you are doing it and know it’s a risk. I recommend trying to be a 1 or 2 standard deviations from the cookie-cutter resume, but not so many as 4+ std dev from average. Depending on the background of the hiring person and the company culture, your beautiful and artistic resume may be seen as not-technical or not-serious enough if your resume is too flashy. On the other hand, for a company that prides itself on style, if your resume is too plain and serious, then the hiring manager may wonder if you have the appropriate interest in fashion to do well as an analytical expert in that domain.

In summary, get resume tips from helpers in your field. Look at as many resumes as you can to spot patterns and see what trends are present.  Then, follow the mold, but be unique! Just like your cover photo: there are standard conventions and how you uphold or break those conventions says a lot about who you are.

Posted in Communicating Math, data science | Tagged , , , , | 3 Comments

Leaving Academics

For academics, mid-winter is a reflective time of year. It was about this time, 3 years ago that I wrote an article reflecting on my own move to industry. Perhaps it’s because this is the time when academic jobs are choosing their applicants. So there is great anxiety wrapped up in “will I have a job come September?” Perhaps it’s because we are 1/2 way through the school year and those papers aren’t written and we have a new crop of confused and needy students.  Or perhaps it’s because it’s dark and cold and oppressive outside… and, ya’ll, it’s just SO snowy this year.

For me, this year in particular, I have many friends who are talking about leaving academics. Women mostly. Scratch that, they are women completely. I think this year is a confluence of political depression and the magic 5 year mark.  We’ve all been done with our PhDs for about 5 years. 10,000 hours we’ve spent in our respective careers; me in Industry, them in Academia. Though, to be fair, they have probably spent more hours on their careers than I have. And how far did it get each of us?

In this way, I get this very personal view into the leaky pipe problem. I see individuals making choices about their life and their adorable newborns and their priorities. I coach individuals as they make their transition into Industry. And I have come to a few conclusions.

For one, I think there is a messaging bias in Academics. While I was in academics, I was bombarded with the message that life outside academics was worthless. Simply bombarded. Not one of my professors enthusiastically agreed with my choice. But why would they? They chose to stay in academics. Almost by definition, they would not coach someone to leave academics. It’s also counter productive for a professor to coach their students to leave. The professor’s success is measured by the success of their students within academics.

Secondly, as one begins to leave the academic campus, it feels like she are falling off a cliff and she can never go back. Because, like, no one ever does! There are very few examples of individuals who went to Industry and then returned to a good position in Academics. Initially, I thought this was evidence that it wasn’t possible. Somehow being in Industry soils you to the purity of Academics. It’s a one-way trip! Be extra sure you want to make that choice because there is NO GOING BACK. But now, 5 years later, I have a different theory to explain why no one returns to the ivory tower. It’s because people do not want to!

Academics requires a brutal commitment level. It’s like being a professional athlete (the odds are about the same). Except, that you almost never get to win a game, but somehow you have to keep trying. It’s hard. And so, the decisions feels like a choice: “Quit” or “Don’t quit”?  I disagree with this framing. In Industry, we have these phrases for people who want to change jobs. You can either be running from something you hate in your current job or running towards something that you’d like in your new role. The Quit/Don’t Quit framing means you can only run from Academics, but you can never run towards Industry.  But what if Industry is actually a super lovely place to be?

Turns out, for me, Industry is a good place to be a balanced adult. I work during the day and spend time with my family at night and on weekends. I am happy. Even the most academically minded colleagues I have out here in Industry balance their desires by doing independent contracting for the government or some other more “academic” activity. I don’t know anyone who has retroactively wished they stayed in Academics. But if that’s you, then please let me know! I would love to learn why you feel this way.

Lastly, I have seen that the decision to shift from Academics to Industry is deeply personal but almost always influenced by a desire for improved mental health. Just recently, the BBS ran a tragic story about a professor who committed suicide because the load was too great. The first two elements: the messaging bias and the one-way trip combine to make it feel like there is no way out. No other options. I believe that the mental toll of being in academics cannot be understated. And finding a path towards a (hopefully) more balanced life, is always personal and unique.

Another, more positive, very personal change that influences people are babies. Babies! Nature just published a statistic that >50% of women leave STEM fields after their first child. Now this study wasn’t just about academics. But I think it raises an important point.  Mother’s brains physically change after birth. [Boston Globe, NYTimes] I literally think differently than I did pre-baby. I have observed a rapid adjustment in my priorities. I just don’t care as much about some things which used to be vital to me. I’m not a totally different person, but just epsilon different enough that I make some different choices. I can understand how someone who was very committed to their academic career could change course once they spend time with their newest family member. There is just some things, for me, which feel more important, more critical than throwing myself at a wall to maybe, possibly, grow the collective human knowledge by an infinitesimal amount.

So, in conclusion, if you are someone who is in Academics who is thinking about moving towards Industry, then take comfort. You are not alone and your feelings are valid. Have some tea and start thinking and learning. (I have some resources collected here that might help you.) Read about other’s journeys and the process of building a LinkedIn profile. You are exceptionally good at learning and understanding new ideas. I have every confidence that if you want to try Industry for a while, that there is a company out there that would love to hire you.


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Raising a Neural Net

I’ve been a little busy over the past year or so. You see, I have been creating the most advanced neural net known to mankind. It took me 9 months to code it and get it just right. My husband helped to provide some of the code, I provided the rest. As often happens, we repurposed some of the code from people we trust. It’s basically open source. Almost anyone can do it. Compiling it wasn’t the most fun I’ve ever had… There were some late nights, and I definitely lost some sleep. But my neural net turned out really cute! I lucked out. I didn’t plan for it, but it has dimples!

Now that she is here, I’m training my model. It will take a long time to train. 18 years by some accounts. Maybe more? I often wonder if the training time depends more on me training it well or its implicit structure?  It has many layers and a variety of activation functions. There is definitely drop out automatically encoded. By all accounts, it’s very sophisticated. The only  major downside is that I have to feed it all its labeled and unlabeled data manually.

When I put together my training data, I think a lot about ethics and values. I want my little neutral net to eventually make good decisions and be kind to other programs. It’s not so easy to build an unbiased model. Because I’m creating the training set, it’s up to me (and my family) to teach it well. Luckily, it’s not a one and done situation. If, in a few years, I learn that my neural net has learned to hit other neural nets, then I can ramp up the training data to try discourage that activity. But, I guess ultimately, it’s still a black box. I’ll never understand exactly why it made each decision!

Having a human neural net makes me think differently about how I might train my digital neural nets in the future… Meanwhile, my neural net woke up from her defragging and sleep processes, so I need to go. I get to go to stack more soft blocks so she can collect more unlabeled data about gravity!

Posted in Communicating Math, data science | Tagged , , , , , | 3 Comments

Are you masculine enough to be a trusted leader?

I’ve been a woman mathematician in Industry for almost 5 years. I have Malcolm Gladwell’s requisite 10,000 hours to be an expert.  Throughout my experience, I have struggled to gain the trust of my more technical colleagues. It’s not a new problem for me, but, upon joining Industry, a new wrinkle to this puzzle has presented itself: Do I want to be trusted as a technical individual contributor or as a leader? I like both options! I am capable of both options. And, as a result, I waffle back and forth between the options.

Every time I’m on one track, I think fondly about how the grass is greener on the other side. I think, “Gosh, I miss sitting quietly at my computer and writing code.”  Or I think, “Man, I wish this project was better managed so my good work was used more!” On the whole, I have chosen to lead people. Because without a good leader, the technical work doesn’t get used. And I came to Industry so people will use my work instead of having the work live primarily in a journal article somewhere.

But, do ‘leading’ and ‘doing’ have to be so separate? In some companies, the two development tracks are presented as something which can be done together. “You can lead people AND write code!” they say. But in my experience, any leader who does this effectively is working 80 hours week. Spending 40 hours on technical contributions and 40 hours on leading people. And, ultimately, when time is an issue, an individual must decide which is more important to them: leading people or doing technical work.

And, based on some new research, there are other reasons to believe that these two options are NOT options which can be taken together. M. Teresa Cardador & Brianna Caza interviewed more than 330 engineers over the last 4 years [HBR article]. Taken together their conversations show that technical folks view managerial roles as undesirable. Teresa has done previous research on the prestige hierarchy of this highly technical space. Our culture teaches us the hard skills we need to be technical capable are separate from the soft skills that make us good with other people. What’s more, we “also learn that these skills are gendered, with the [hard skills] viewed as more masculine, more revered and higher status; and the [soft skills] viewed as more feminine and lower status.” So, as I move into leadership, do I have enough technical skills to be seen as trustworthy on technical topics? Am I masculine enough to be trusted? Without that, my value to the company is seen as lower than the individual contributors because I’m using my “feminine” skills to get work done.

“It seems like these things, these skills, these traits that I’ve honed for a very long time…one might label as soft skills maybe…are not really the kinds of things that get rewarded as much on day to day. Or are being recognized.” – Cardador & Caza

This quote is from the article, but I could have easily said the same thing.  I know many people who agree with this statement. Being a leader is a burden and is ‘less valuable’ than being a technical data scientist. Devaluing leadership isn’t really a problem, until you layer in the gender bias. Cardador & Caza found in their study “that while some women pursued these technical supervisor or management roles based on their preferences, some were also mentored into these roles.” So, women are getting pushed into these roles despite their other preferences.

“When women disproportionately occupy roles that are less valued or unwanted, it can reinforce stereotypes about female engineers being less technically skilled, make them feel less respected, and create the illusion that they are not a ‘real engineer.’” – Cardador & Caza

And that’s exactly how the choice feels to me. Do I want to be a “real data scientist” or do I want to be a leader of data scientists? I find leading to be more personally fulfilling and, I believe, leading makes me more valuable to my company because I will insure the work of the non-leaders finds its best use case. I spent decades of my life politely fighting with men to make them see that my hard skills are just as advanced as the men’s skills are. I spent these years metaphorically saying, “I’m masculine! I’m one of the guys!” But now, with a choice to serve the greater good and use the skills that are really underrepresented in tech (social skills), I am undermining all that credibility I built.

The perception is tough to shake. Cardador & Caza talk about resilience of women in tech. Mostly it’s about staying true to oneself and ignoring the peer pressure. It’s being able to say: “everyone else will think I’m making the less prestigious choice. And that’s OK.” But honestly, I can’t decide if it is OK, because if I lead, then the individual contributors will perceive that I’m more feminine and approachable and therefore less technically capable. And if the team I lead doesn’t believe me to be capable, then I will have a harder time leading the team effectively. And I don’t know how to solve that puzzle… So, the question remains: Do I appear masculine enough to be trusted?

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Oreo Preferences

Everyone loves Oreos! They are delicious and sugary and chocolatey and have excellent filling!

For your pleasure, I have done extensive research on the preferences of several different kinds of Oreos. Well, maybe not extensive research. But a bunch of research anyways!

I did a small study to compare Oreos, Oreo Double Stuf, and Oreo Thins. Here are some basic stats about the cookies in question:

Name Calories/


Serving Size Calorie/


Servings per box Cookies Calories per box
Oreo 160 3 53.3 12 36 1920
Oreo Double 140 2 70.0 15 30 2100
Oreo Thin 140 4 35.0 10 40 1400

I had 21 participants. Only 4 of my participants regularly pulls their Oreos apart. But 41% of the participants dunk their Oreos in milk! So the milk tradition is still going strong. Of these 21, 10 preferred double stuf, 8 preferred classic and 3 preferred thins.

I wanted to understand the overall preference between these cookie types versus their caloric intake. I want to determine if this data would change anyone’s mind about which cookie they eat.

I am a die-hard Double Stuf eater. I don’t understand why anyone would eat a classic Oreo. There is far too much cookie for the amount of filling. However, there is the disappointing truth that my cookie choice costs 16 calories more per cookie. And I only get 30 cookies in my box of cookies. If I’m trying to count calories the question is: do I love a single double stuf cookie 30% more than I love a single regular Oreo?  For me, there is no question. I absolutely do. Lets get into the weeds: if I prefer double stuf and my enjoyment level is an 7, but my enjoyment level of a classic is a 4. Then I enjoy my double stuf 7/4 -1 = 75% more than I enjoy the classic. So, even if I’m watching my calories, I shouldn’t switch to classic.

My participants were given a single cookie of each type as asked to rate their preferences in order (1st, 2nd, 3rd) and then to rate their enjoyment numerically from 0 to 10.  So I can also consider their answers. Do those who prefer double stuf prefer it by more than 30%?  Of the 10 people who prefer double stuf, only 5 prefer it by more than 30% of the value they gave classic. Thus, the 5 for whom double stuf is not vastly superior, they should probably stick to classic.

What about thins? An Oreo thin is only 50% of the calories of a double stuf and 66% of the calories of a classic. Of the 8 people who chose classic as their preference, do they prefer it 50% more than a thin? Only one person rated the classic more than 50% higher than they rated the thin.  Thus, 7 out of 8 people who prefer classics actually gain more enjoyment/calorie from an Oreo thin.  Additionally, 6/10 people who prefer double stuffs actually gain more enjoyment/calorie from an Oreo thin.

What about me? I was surprisingly pleased with the taste experience of the Oreo thin.  I gave the thin the same value as the double stuf: 7/10.  Therefore, if I want a single cookie, it’s actually WAY better for me to get 7 points of enjoyment out of a thin than a double stuf. As a result of this study, I buy Oreo thins instead of double stuf. What will you do?

In conclusion, although thins were the least desirable (only 3/21 people preferred them most), if you only get 1 cookie and you want to optimize your enjoyment to calorie ratio, then most people should buy thins. 16 out of 21, or 76%, of my participants would get more enjoyment/calorie from a thin than from any other Oreo.



Posted in Communicating Math | 3 Comments

PhDs with Personality

Everyone has a personality.  At the risk of over-simplifying, I’m going to argue that there are two types of PhDs in Industry. Front-Room PhDs and Back-Room PhDs*. The distinction is that some technicians and developers enjoy talking to business clients and some do not. Your back-room PhDs probably don’t want to talk to the clients anyways, they want to write code and wear really comfortable clothing. The front-room PhDs have a desire to communicate across disciplines and bring the two groups (developers and clients) closer together.

I first came upon this phrase while reading Competing on Analytics, by Jeanne G. Harris and Thomas H. Davenport. They spend a little time talking about how companies can use analytics to propel their business forward.

“The need is for analytical experts who also understand the business in general and the particular business need of a specific decision maker. One company referred to such individuals as “front room statisticians,” distinguishing them from “backroom statisticians” who have analytical skills but who are not terribly business oriented and may also not have a high degree of interpersonal skills.

In order to facilitate this relationship, a consumer products firm with an IT-based analytical group hires what it calls PhDs with personality– individuals with heavy quantitative skills but also the ability to speak the language of the business and market their work to internal (and in some cases, external) customers.” -Competing on Analytics Pg 144

I think the individual who can understand the technical details and successfully communicate those details to the business is a rare person.  Usually it takes 2 different people to translate from technical speak to business speak. And these two people have to be really good friends and spend a lot of time talking to each other to get an accord. It’s like if you wanted to translate from a group of English speakers to a group of Japanese speakers, but the only language in common was Italian. So, you first translate your ideas into Italian and then hope the other person knows enough Italian to get the idea appropriately translated into Japanese. Small wonder things get lost in translation! The value of having a single individual who knows both languages is vast.

Aside from just reducing the number of steps in the corporate game of telephone, this person can add value and insights to the translation as well. They can say things like, “I know you really want this work completed by date X, but we’re going to have to reduce the scope of the project to get it complete by then. I can work with you to understand which pieces of your request are hard and which are easy to help create an appropriate acceptance criteria for a minimal viable product.”

The other major benefit has to do with the intrinsic value of having a seat at the table. There is a lot of conversation about how data teams can’t drive decisions if there is no one at the business table who can listen to what the client’s challenges are.  It’s critical to get a seat at the table. PhDs with Personality are the ideal type of person for this situation.  For, she (or he!) can sit at the table without pissing anyone off.  She doesn’t get lost in the weeds of the technical details and she can speak about the results of the work instead of just the technical process. She can listen and show empathy to the clients, without giving up her empathy for the developers. It’s hard to have a seat at the table if your representative keeps derailing meetings and upsetting potential clients.

By having a good balance of back-room PhDs and front-room PhDs, a team can be much more successful and complete projects that are more like to move the needle for your clients.

*I’m using the term PhD loosely here . I don’t think this discussion is limited to just PhDs, because there are many people who work in the hyper-technical space who don’t have PhDs.


Posted in Communicating Math, data science | 4 Comments

Marketing Academic Strengths in Industry

So you wanna get an Industry job?  Great! But how do you begin to translate your CV into something non-academics want to read? How do you market your strengths?

First, we have to acknowledge that your CV took years to put together. Not just years to get experience, but years of tweaking your communication to present your best self.  Only now, the objective has changed. Instead of communicating to the 3-10 other academics who care about your area of specialization, you need to communicate your CV to people who may not have any graduate academic experience. Don’t expect to get your academic CV turned into a Industrial resume in an afternoon. It’s going to take some effort. But you have a graduate degree, you know how to put effort in.

Second, we have to acknowledge that hiring managers will spent less than 30 seconds looking at your resume.  And they tend to read the most in the upper left hand triangle of the page, and the least on the lower right.  Optimize that space. For starters, put that “, PhD” right after your name. So, if they ONLY thing they read is your name, then at least they know you did the PhD thing. Then, don’t fill the left hand side of your resume with dates, as some resume builders recommend.  Instead put your job titles over there, and make them stand out.

Finally, the most complicated part of your task is to understand your work and the Industry well enough that you can capitalize on the overlapping skills. Or, what I like to call, “Finding your dissertation sentence.”  You need to find one sentence that describes to a layperson what your work was about. For example, my dissertation was titled: “Forced Oscillators with Dynamic Hopf Bifurcations and applications to Paleoclimate.”  However, in my objective statement on my resume, I say, “My dissertation focused on identifying the driving factors in complex systems like our planet’s climate.” I spent weeks trying to figure out how to make an 90 second elevator pitch for my dissertation work.  And then more weeks streamlining it down to one sentence: “Identifying the driving factors in complex systems like our planet’s climate.” There is is. Six years of work, in one sentence.

I strongly encourage you to find your sentence.  No matter what Industrial field you go into or what your dissertation was about, you will benefit greatly from knowing your sentence. How do you find your sentence? I recommending talking about your dissertation to everyone. Mathematicians in and out of your field. Talk to you parents, pets, friends, or that guy who always talks to you on the bus (well, maybe not him- use your best judgement!).  Watch your listener for when their eyes get glassy and they tune out. When that happens you know that your explanation is too complex or too long, or both.

As you practice, first focus on the how you did your work. You may never need to prove another statement about complex fields again, but you do need to be able to clearly communicate your logic about a business problem.  You might need to be able to extrapolate to abstract concepts to allow a solution that worked in one area of your company to be applied in another. There are concrete transferrable skills that you have learned in your PhD.  But, you’ve been so problem focused (because, you have to actually finish your PhD!) that you probably haven’t noticed the skills you are learning along the way.  These skills, that you can’t yet recognize, are your strengths.

By way of example, here is a skill that every PhD has: You can do something really hard for a really long time.  This is a fundamental strength of everyone who has earned a PhD. What else is specific to your field and your experience? Keep telling your story until you figure that out!

You also need to focus on the outcomes of your work.  I proved that a popular, long-standing model will never be able to reproduce all the features of our planets δO18 data. This isn’t something that very many people on the planet actually care about, but the Industry hiring manger can understand the impact of my work.  I proved that this model formulation wasn’t valuable. Thereby making the other popular models more likely to be telling a true story about our planet. What does your work do? Try to say your outcome without using any math terms. Replace every math term with “Thing” or “do-whats-it” and then work to find non-math terms that can fill in the gaps and still make sense.

Your sentence will not come together in one day, it will take time. But you have skill that you can do something really hard for a really long time!  Apply that focus to the art of translating your work into standard english, and you’ll be a long way towards marketing yourself outside of academics.

If you would like additional context about moving into Industry or becoming a data scientist, I have a section of Social Math devoted to Data Science.  Additional explicit advice on how to write your resume I recommend 10 Things Smart PhDs do NOT Put on their Industry Resumes from CheekyPhD. It’s a really clear summary of the first steps towards making your Industrial resume .


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