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Wait, what? The 15 most commonly misunderstood job titles

A study asked the parents of ~1,000 individuals to explain what their child does for work. We present to you: the 15 most commonly misunderstood job titles.

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The job market is evolving

As we know, each generation comes with its own form of jargon. Phrases such as “YOLO,” “squad,” and “turnt” may not have resonance in meaning for people over a certain age.

However, this phenomenon does not just apply to slang terms. As generations evolve, so does the job market. With this, job titles change through the course of time.

Business Insider recently compiled a list of the 15 most commonly misunderstood job titles. Fully 1,390 parents were surveyed, and almost half of them were unfamiliar with what their child does for work.

Below is the list of the 15 most commonly misunderstood job titles according to the study. The percentages next to them reflect the number of parents who did not understand the title.

The 15 most commonly misunderstood job titles

1. User-Interface Designer: 97%
A user-interface designer is responsible for creating technology that is easier for people to use.

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2. Data Scientist: 95%
Data scientists analyze information (data) in order to help leaders make stronger and better decisions for their company.

3. Actuary: 94%
This job involves using statistics as a way of estimating how much it costs to insure people.

4. Social-Media Manager: 93%
A social-media manager works with a company and develops and maintains their social media presence.

5. Public-Relations Manager: 91%
Public-relations managers work hard to present a positive image of their client in the public eye.

6. Radio Producer: 90%
A producer of a radio station is tasked with choosing music and booking guests for radio shows.

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7. Sociologist: 89%
A sociologist works to learn about people’s beliefs through conducting surveys and interviews.

8. Sports-Team Manager: 86%
The manager of a sports team works along side the coaches and players to make sure everything is running smoothly.

9. Civil Servant: 85%
A civil servant is one who works for the government, but not necessarily for the uniformed services.

10. Engineer: 85%
Engineers are critical thinkers who are tasked to solve problems mechanically, civilly, or medically.

11. Fashion Designer: 85%
These are the people responsible for designing clothing, footwear, and accessories.

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12. Investment Banker: 84%
Investment bankers create relationships between those with money and those who need it.

13. Laboratory Technician: 84%
These technicians perform experiments and analyze their results.

14. Sales Executive: 83%
A sales executive searches for people who can benefit from a company’s product.

15. Software Developer: 83%
Software developers work to design and improve software programs.


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Staff Writer, Taylor Leddin is a publicist and freelance writer for a number of national outlets. She was featured on Thrive Global as a successful woman in journalism, and is the editor-in-chief of The Tidbit. Taylor resides in Chicago and has a Bachelor in Communication Studies from Illinois State University.

1 Comment

1 Comment

  1. Sean McClure

    November 17, 2015 at 7:49 pm

    Although I agree the title Data Scientist is largely misunderstood, this article does little to clarify the name. What is defined in this article is
    Business Intelligence (BI), not Data Science.

    It is a common misconception that these two fields largely overlap. In BI, analysis and statistics are used to summarize historical data so as to
    produce a report (e.g. dashboard) regarding how an organization has performed up to the current date. The hope is that by exposing these
    trends and patterns the decision-makers of an organization will be able to make more informed decisions. The defining characteristic in this scenario
    is that the onus of decision-making is left entirely to the human end-user. They are responsible for somehow converting all that summarized data
    into a decision that will produce value.

    The purpose of Data Science is to offload aspects of the decision-making process to the machine. The data scientist is primarily concerned with building
    ‘data products’ that use machine learning (a practical application of AI) to automate decision-making in a way that is not possible using hard-coded business
    rules (i.e. traditional software development). The raison d’etre for the Data Scientist is that many real-world problems are ‘high dimensional’ in nature,
    which means there are too many variables for the human mind to fully comprehend, and therefore make decisions from. Decision-making now requires
    an ‘intelligent machine’ (the data product) to internally summarize and detect patterns, and produce an output that maps directly to a decision such
    as a classification, or a prediction (as opposed to simply producing a dashboard and hoping the end user can make an informed decision). This requires building models that can explain and predict the environment these data products operate in. Since a decision is “a conclusion or resolution reached after consideration” we can think of a classification or prediction as a conclusion reached after the machine has ‘considered’ the data (in a way the human user never could). So the defining characteristic of Data Science is the offloading of decision-making to a data product that supports the strategy of an organization.

    Businesses now look to Data Scientists to support the decision-making process by building data products that offload the difficult task of
    understanding large amounts of complex data to automatically make decisions. This introduces a new suite of products that are used for a range of
    computing tasks where designing and programming explicit instructions is not possible. Examples include Spam filtering, Optical Character Recognition, Facial Recognition,
    Fraud Detection, Recommender Systems, Sentiment Analysis, and so on. Any application that produces a functionality that cannot be arrived at by
    using hard-coded deterministic rules.

    Many professionals analyze data and draw conclusions to support an organization. This is not the defining characteristic of Data Science. When faced
    with a problem that involves data and analysis, ask yourself if it requires a machine to automatically draw conclusions about its environment. Ask yourself
    if the features needed in the software (like OCR, or image detection) cannot be arrived at using explicit programming. Ask yourself if some aspect of
    the decision-making process has been offloaded to the machine. If so, then it is Data Science.

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