Landing that dream data scientist position

The data scientist role emerged as a sought-after position during the 2010s.

Demand for data scientists is continuing to grow. Mr. Khaled Hasna, a technical recruiter with IT-IQ Inc. observes, “significant growth in demand for data science in the finance and insurance sectors – anywhere where risk assessment is required.”

The numbers back this up. The data scientist role:

  1. Topped Glassdoor’s top jobs list from 2015 to 2019.
  2. Job postings increased 75 per cent between 2015 and 2018.
  3. Continues to lead the tech industry in starting salary.
  4. Is a creative, challenging, rewarding position.

What’s not to like? Start by checking your aptitude for data science by taking the short practice test at Mocha Skill Testing Solution or at the Ambeone Training Institute.

Here’s how to land one of these exciting positions that will accelerate your tech career.

Formal education

Typically, prospective data scientists first add to their education. The major alternative routes, that are equally valuable, to becoming educated as a data scientist are:

  1. A master’s program that offers theory-rich learning in a structured environment with the full credentials of a university, perhaps prestigious, degree. This alternative, unlike the others, requires a bachelor’s degree as a prerequisite. Click here for a list of data science degree programs.
  2. Self-taught through a series of MOOCs that requires lots of motivation and discipline to complete. Click here for a description of 10 leading MOOC providers.
  3. Bootcamps that provide the benefits of credentialing and social learning of a master’s degree. Bootcamps operate at an accelerated rate with a stronger emphasis on experiential learning. Click here for a long list of available boot camps.

The master’s program and boot camps offer the additional advantage of building a professional network with fellow students and perhaps instructors.

On the other hand, Steve Morcom, the CEO of Teamit Inc. says, “business clients don’t care much about formal education. They’re more focused on the skills and experience present in our teams. They expect considerable data management proficiency and statistical expertise”.

Steve’s firm provides proven, remote Canadian software teams to USA tech companies.

Informal education

A tantalizing alternative to formal education to consider is simply learning on the job. In the current hot job market where the supply of experienced data scientists doesn’t come close to filling the demand, some organizations have given up on hiring and are resorting to a more unorthodox approach. These organizations simply assign some of their brighter analysts and developers to attack business problems with data science solutions and learn as they go. If you want to become a data scientist, perhaps all you have to do is raise your hand and join this team.
This idea is not as crazy as it may sound because:

  1. There’s an enormous number of free online videos and tutorials about data science on the Web that can build the skills of budding data scientists.
  2. A significant number of AI software platforms are now available to simplify the development of data science application. Read these articles for a quick introduction to the Top 18 Artificial Intelligence Platforms and the Best Data Science and Machine Learning Platforms.
  3. Most cloud service providers also offer a large number of data science software routines that are easy to incorporate into models developed by neophytes. For example, AWS offers H2O Driverless AI, Google offers AI Platform, IBM offers Watson Studio and Watson Machine Learning, and Microsoft Azure offers Data Science and AI Development.
  4. You can learn a lot by attending a few data science conferences. Here’s a list.

Technical skills

One of the difficulties associated with landing a data scientist position is the long list of technical skills associated with data science. Prospective employers, being unsure of what skills they will need, tend to make the list overly long in the position description.
That long list can be intimidating to some candidates and likely reduces the pool of applicants too much. The reality is that few candidates can credibly claim they are familiar with even half of the skills on even this abbreviated list:

  1. Automating machine learning
  2. Data analytics, data visualization and BI
  3. Statistical, predictive modelling
  4. Data modelling and data mining
  5. Data warehouse and data integration

For a detailed list of technical skills associated with data science and related positions, click here. So, don’t let a small gap in your skills keep you from applying to a position that is grabbing your attention.

Gus Walker, the senior director of product management at Veritone, Inc., a publicly-traded AI platform company, checks to make sure data science candidates “follow the scientific method – meaning they follow what the data says. I don’t like candidates that follow their hunches, intuition or emotions.”

Soft skills

Data scientists have a reputation as prima donnas even though almost none can actually sing opera. However, data scientist candidates are more likely hired if they exhibit a little humility and these soft skills:

  1. Superior communication skills
  2. Curiosity
  3. Creativity
  4. Grit

Mr. Gus Walker recruits “data scientists who can not only accomplish great work but can also communicate their work in ways the team they’re working with can understand”.

Domain expertise

Every prospective employer operates in a particular industry, non-profit sector or government department. Every employer prefers to hire data scientists who have already accumulated at least some expertise in their particular domain.

The elapsed time and cost required to train someone in the details of the domain are prohibitive for almost all prospective employers. The result is a trade-off for both data science applicants and for employers.

Prospective employers will typically accept data science applicants with gaps in their skills when they can demonstrate at least some expertise in their particular domain.

Marc Boulet, data science and management lead, oil sands geology at Cenovus Energy Inc. says his “biggest challenge is finding a data scientist with both domain expertise and a strong data science skillset. It’s relatively easy to find someone who is well-versed in either field. However, finding a suitable candidate who can successfully apply bleeding-edge analytics capabilities to well-entrenched challenges within the energy industry is not a trivial task”.

Data science experience

Employers prefer experienced candidates because that tends to accelerate projects and reduce risk. However, data science is a relatively young discipline. That reality reduces the number of years of experience anyone can point to.

As a candidate, you might as well apply even if you don’t have all the years of experience stated in the position description. In many cases, the prospective employer is probably dreaming, and you may have the most experience of all the candidates in the applicant pool.

Mr. Steve Morcom likes “selecting junior candidates right out of school. While we experience no shortage of applicants, this approach only works because we ensure strong, experienced leaders in our development teams that can mentor new employees carefully”.
For a better sense of where data science is heading, please view this slide show: 5 AI trends to monitor in 2020.

What strategies would you recommend to prospective data scientist to land their dream job? Let us know in the comments below.

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Jim Love, Chief Content Officer, IT World Canada
Yogi Schulz
Yogi Schulz
Yogi Schulz has over 40 years of Information Technology experience in various industries. Yogi works extensively in the petroleum industry to select and implement financial, production revenue accounting, land & contracts, and geotechnical systems. He manages projects that arise from changes in business requirements, from the need to leverage technology opportunities and from mergers. His specialties include IT strategy, web strategy, and systems project management.

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