Because Soap and Chocolate are Two Different Things: Context is Key to the Job Search

Because Soap and Chocolate are Two Different Things: Context is Key to the Job Search

If you showed up at New York’s Beacon Theatre on August 17, ready to see the Beach Boys, and a heavily bearded, former MLB fastball pitcher took the stage…you might be perplexed.

If a member of the iconic Seinfeld cast married Britney Spears in what would become a 55-hour marriage…you might be surprised.

If on a sunny fall Sunday, the director of Clerks and Mallrats entered Ford Field as the running back for the Detroit Lions…you might be worried.

And if while allowing yourself one decadent cheat treat from your usual sugar-free-Paleo-raw-foods diet you bit into a hunk of one-quarter-moisturizing-cream soap…you might be pissed.

Brian Wilson, Jason Alexander, Kevin Smith, and Dove all agree with you. All eight of them.

Same name, same pronunciation, same spelling. Vastly different skills and offerings. Context matters, because soap and chocolate are two different things.

Job titles are similar. They often use the same words, use the same pronunciations, feature the same spellings. There is no patent office for job titles, ensuring specificity (that would be a mess, and also, patents didn’t help a ton with that whole soap/chocolate thing). And like naming a child, there is no regulatory office for naming uniqueness. The job title of Data Anything is dependent on context. Data Scientist. Data Analyst. Data Miner. Data Ninja. All two-word job titles that need extra words to explain and nuance. Before you even think about going through the process of applying, it’s all of those extra words that you need to ensure align with your interest and skills. Job titles are like Brian Wilson – before you buy a ticket, make sure you are seeing the right one

It’s a Wednesday afternoon as I write this, and I turn to the internet to back up my case. A quick search of Job Title: Data Scientist turns up multiple pages of examples, one job title double-dipping into multiple roles, industries, and teams. A job posting from Esurance (an Allstate Company, HQ’ed in San Francisco) shares that the company has “created a centralized data science group that is responsible for helping business units make objective decisions using science. The Group supports overall business operations by delivering critical analytical insights and in-depth consultative analyses.” This is a Data Scientist on a central Data Science team, working alongside Data Science peers and supporting the entirety of the company. A few scrolls down the page (and a few blocks down the street), Wells Fargo is looking for a Data Scientist with the following description: As a senior analyst in Wells Fargo’s Enterprise Analytics group, you will be responsible for pushing the state of the art in organization-wide natural language processing (NLP) and speech analytics applications. As an in-house expert in NLP and speech analytics, this position involves working across Wells Fargo lines of business to determine useful ways in which NLP and speech analytics can solve critical business problems and satisfy customers’ financial needs. The title of Data Scientist with an internal title of senior analyst, and a focus (in-house expert) on natural language processing. More specific, more specialized. Same title as the first one.

Taking this websurfin’ of the USA down to Austin…

IBM Watson is looking for a Data Scientist “as part of the Watson Implementations organization, which will deliver business value from cognitive solutions to our clients. We tackle the application of machine learning and data science in customer engagements. Watson Data Scientists drive deep collaboration with clients, drive data-driven decision making and implement cloud-based cognitive solutions that bring quick business value.” This is a client-facing Data Scientist, so maybe a similar function to that of the Esurance role, but a different application and work scope.

And landing on New York looking for where more examples may be, one more time: New York. The online video sharing platform Dailymotion seeks a Data Scientist, specifically focused on problems related to online advertising, and they require “an understanding of online advertising technologies and ecosystem (Ad-Tech Background).” Their data set is specific, their scope is specific, their intention is specific—this is not the general role seen above.

One job title, multiple job roles, needs, qualifications, offerings. Context is key, and specificity is paramount, for both candidates and companies. If you are a Data Science students in Galvanize’s Immersive program, we work with you to establish your intent – your specificity – for your Data Science career. Do you want to work on a central team, or within one product? Do you want to be in-house or third party? Do you want to leverage your previous industry experience into your new technical role? We ask you to start to form that intention so that you don’t spin wheels applying to just any Data Science role—we want you networking, applying, and interviewing into the right Data Science roles. The ones that fit your interests and skills, the ones with the context that fit your career target; it’s the context of the data that is paramount.

Read, research, learn, and listen. Understand the nuances and keywords that live behind those job titles, well before you dive into a job search. Because like doves, Jasons, and Kevins, Job Title: Data Scientist often needs context and clarification to truly be understood.


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