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Myth vs. Fact: Hiring Non-Traditional Data Science Talent

The Data Science job market has become a varied and complicated, yet promising space...

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Myth vs. Fact: Hiring Non-Traditional Data Science Talent

The Data Science job market has become a varied and complicated, yet promising space. Glassdoor’s Best Jobs in America Report for 2019 found that the field of Data Science has the ideal mix of job satisfaction, earning potential, and, of course, number of job openings. However, the growing need for competent data scientists is outpacing the number of trained data scientists on the market.

As a result, hiring managers face significant challenges when it comes to securing talent. This rapid growth in the Data Science field has caused an increased need to hire more diverse, non-traditional candidates. However, a bias towards hiring those with advanced degrees in a directly related field often persists.

Assuming that only professionals with technical advanced degrees can work as data scientists creates a homogenous team and slows down hiring. There is immense practical and perspective-building value in hiring non-traditional data science talent. In this post, I’ll be sharing a few common misconceptions about hiring non-traditional data science talent, along with data to refute these flawed ideas. So strap on your hiring hat, pour some coffee, and let’s start the demystification.

Myth: A data scientist trained outside of the traditional university setting will not have strong enough skills.
: Non-traditional training programs are designed to train junior data science talent in a robust and rigorous way.

Tell me more: Technical schools and bootcamps, especially those that (1)are longer and more in-depth than the typical 12-week program, (2)are taught live, and (3)have an applied component, are designed to train data scientists who can immediately succeed in industry.

Students trained at Lambda School, for example, not only have a high bar for admissions but also have to continually pass technically challenging week-long sprints in order to continue in the program. Only after demonstrating technical mastery of the core curriculum topics will they advance to complete their training.

Myth: A professional without a degree, or with a non-technical degree, will be a weaker candidate.
Data Scientists from non-traditional backgrounds possess work and educational experience that will bring a holistic perspective, domain knowledge, and honed soft skills to the team.

Tell me more: Given that we have established that technical skills can be robustly taught outside of the traditional setting (see Fact #1 above), let’s jump into the immense benefits that a Data Scientist without a degree or with a non-technical degree can bring.

  • Professionals who did not attend university spent that time getting extra experience in the workforce and developing critical soft skills. Data Science job seekers with more work experience tend to particularly excel at communication with non-technical stakeholders, client relationship management, collaboration, and multitasking.
  • Data Scientists who have  liberal arts degrees not only bring many great soft skills but also bring extensively honed critical thinking and decision making skills. Ask any B.A. in Philosophy, Economics, or Psychology how their training informs their work as a Data Scientist and you will be blown away by the competitive advantage their viewpoints can provide.

Myth: A data scientist without a degree will not be able to think critically about difficult technical decisions.
:  Students in non-traditional programs are not only trained on how to make critical technical decisions, but they also demonstrate this skill throughout the applied and consistently updated curriculum.

Tell me more: Data science training programs outside of the university setting actually have more flexibility in how they train students. Technical training schools are able to make real-time changes to the curriculum to ensure that students are learning exactly what they need to know to make difficult technical decisions in industry. This is particularly important given the speed in which technical tools change and new tools are introduced.

Jon-Cody Sokol, Data Science Instructor at Lambda School and previous Consultant at Deloitte explains, “We teach students to focus on how data science methods solve problems instead of focusing on the methods themselves.” This problem solving lens can be applied to many decisions in industry. By learning in a solutions-focused way, students are empowered to continue growing their toolkit of methods so they can continue to make the best decisions.

Myth: A career changer is less invested in the field of data science.
Career changers are particularly strong Data Science candidates because of their industry perspective, focused technical training and soft skills from prior professional roles.

Tell me more: Many career changers specifically saw the challenges that their previous companies had with data and used that as motivation to study Data Science and return to industry with the ability to solve these problems. They often begin their retraining by learning coding and advanced math in the evening while working full-time. With this drive and self determination, they retool their core skill set with a mature understanding of the market and how their values and passions align with the Data Science Industry.

As the field of data science grows and you want to diversify your talent pool, consider hiring non-traditional data science talent. Who knows? You may just locate the mix of soft skills and technical acumen you have been struggling to find!

Rachel Cohen is a Lambda School career coach with a passion for helping people transition to technical, interesting, and well-paying roles. She has been coaching data science students for 5+ years, with previous experience in career counseling and technical recruiting. You can learn more about hiring non-tradition DS talent by connecting with her on LinkedIn.

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