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

Serving Size Calorie/

cookie

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.

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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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