Dream Data Science Job vs. Dream Data Science CandidateNov 05, 2022
A short disclaimer. I do not fully believe in the notion of a dream job. I do believe in dream candidates though. In my opinion the data science career services field needs to focus more helping employers find, cultivate, develop, and promote dream candidates.
With that clarification in mind. Being a dream candidate (in data science, machine learning, artificial intelligence, or advanced analytics) is the best way to finding dream jobs. This article is all about how to be a dream candidate. I promise you, you are dream candidate material. My work in helping others see that in themselves and helping employers see this in their candidates is some of the most rewarding work I can imagine.
The best way to be a dream candidate is to do everything you can to make it easy for the employer to hire you. Make it easy for the employer to focus on you. Make it easy for the employer to remember you and also for others already at the organization to remember you. Make it easy for the employer (their recruiter, hiring manager, and other current employees) to want to keep speaking about you after the interview.
The following is a run-down of how you can be that dream candidate. When you have the skills, elevating yourself to dream candidate status really boils down to two things. First, a solid professional image. Second, a professional portfolio. Let me explain this in more depth.
Maintaining a professional image is important in any field. Because you are aiming to be a dream candidate, in data science (a highly crowded field) it is especially crucial for you. Having a professional image can help you stand out from the competition and land your dream job.
In years gone by, not all that long ago, the advice was simple. Something like "there are several things you can do to maintain a professional image, including dressing appropriately for work, behaving professionally at all times, and networking with other professionals." For better or worse, most of this advice is not outright wrong but it also reinforces gendered and racial stereotypes that exclude women, BIPOC, and others from the field.
Instead let's focus on more modern and contemporary techniques.
- Your social media needs to be up-to-date. Ideally you'll have current photos with representative hair styles plus neat and tidy wardrobes. Your photos will be clear, not blurry, and well lit. Also, you will have regular, consistent, and recent activity that showcases your interest in data science, machine learning, artificial intelligence, and advanced analytics. It is not necessary to post every day. You have the ultimate power to decide what will constitute regular and consistent. I recommend at least a few times per month. If you are a client of mine, I'm encouraging you to post updates at least a couple times per week (and it isn't unreasonable to post every day). I know some folks in the field who regularly post multiple times daily.
- Your email address should also be professional. This is especially important for mid- or late-career professionals who first created an email address 10, 15, or 20 years ago. If you are still using something like "[email protected]" or "[email protected]" it is time for an update.
A professional portfolio is the secret sauce. When you have an interesting and thoughtful professional portfolio - this is what will make it easy for the employer, their recruiters, and your prospective coworkers easy to remember you by. A portfolio makes it easy to talk about you.
You might say, a professional portfolio is a collection of your work, experiences, and accomplishments that you can share with potential employers. It can be in any format, but most often it is a digital portfolio that is easily shared online. A professional portfolio is a great way to show off your skills and experience in data science.
I advise clients to pursue a distributed portfolio approach. A distributed approach means that you will place portfolio entries in a variety of locations online. The go-to sites and options for building a distributed portfolio as a data scientist are GitHub, Medium, LinkedIn, Quora, Reddit, StackExchange, KD Nuggets, Open Data Science, and other related websites.
If you need or want help on how to prepare content for your portfolio here are multiple places that will provide sources of inspiration:
- How You Can Add To And Enhance Your Programming Portfolio
- Write about software you don't like, or that doesn't work as expected. Frist example: A Few Times, I’ve Broken Pandas. Second example: Merging Data: The Pandas Missing Output.
- Write a coding cookbook: A Cookbook: Using Distance To Measure Similarity.
- Make and distribute a fictional data set that others can use and learn with. See for example: How To Make Fictional Data. And also see: Three More Ways To Make Fictional Data.
- Make a "Rosetta" stone that involves replicating the same project in multiple tools. For example I wrote From Scatter Plot To Story in Stata, Matplotlib, Seaborn, and Google Data Studio.
- Make a quick reference (also sometimes known as a cheat sheet).
In order to land your dream data science job, it is important to have a strong professional image. One of the best hacks to landing that dream job is to position yourself as a dream candidate. You are a dream candidate. It is the professional image that will help you communicate your status as a dream candidate.
To be a dream candidate you need to things including a strong professional image and a professional portfolio. Building or improving upon your strong professional image can mean making simple adjustments. For example updating your social media profiles and switching to a professional email address. A professional portfolio should showcase your skills and experience in data science, and can be distributed across a variety of online platforms. This article also provided several examples of ideas that will inspire you as you look to build your professional portfolio.