What Employers Want

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WADSIH

What Employers Want

With more data being generated than ever before, data science has become a rapidly growing and evolving field. As more and more organisations shift their focus to data literacy and deriving data-driven business value, the demand for data scientists, and data science graduates, continues to grow. But what exactly are employers looking for in a data scientist? And how can data science graduates prepare themselves for a successful data science career?

From our discussions with over 100 Western Australian organisations, and interviews with various stakeholders as part of the Data Science in Western Australia report, it was discussed that when looking for data science talent, their search was not restricted to candidates within Western Australia or even Australia. The search for the right talent was often global. This was due to the fact that employers were looking for a specific set of skills.

The top 3 things that Employers want in a data scientist.

  1. Technical Skills

Technical Skills are important for many roles, and none more so than that of a data scientist. Graduates have generally developed a foundation in computer science, mathematics, statistics, and analytics, with many dabbling in coding, but employers want to see these skills developed and put into practice.

One skill that employers are seeking is coding experience. Experience with coding/programming tools such as Python and R are in high demand from employers. Outside of formal education learnings, individuals should further these skills through online courses, upskilling programs and workshops.  Once these skills have been developed it’s essential that they are put into practice in order to gain the proper experience that organisations require. Online hackathon/datathon platforms such as Kaggle are a fantastic way to gain this experience, offering coding competitions and challenges, and a huge repository of data and code to sink your teeth into.

A reasonable understanding of statistics­ is also highly sought after. Organisations are turning to data-driven decision making, so a sound knowledge of statistics and statistical techniques ensures that data scientists can understand or interpret the data effectively. Like most technical skills, statistical skills need to be practiced and strengthened, potentially through upskilling programs, bootcamps and short-courses. A strong statistical knowledge helps data scientists better understand the data they are working with, and in turn results in a better analysis and better outcomes for their organisation.

 

  1. Soft Skills

With the importance of technical skills taking a front row seat, it’s easy for the development of our soft skills to get pushed aside, particularly in a computer science oriented discipline. Soft skills include those skills that we need to interact with colleagues and stakeholders, and overcome problems and challenges. Communication, teamwork, time management, creativity, and adaptability are just a few of the soft skills that employers prioritise. Why? Because they make you more successful in the workplace. An internal study by Google, that analysed their teams’ characteristics of success, found that the most important and productive new ideas didn’t come from their top scientists. Instead, their highest performing teams comprised of employees who brought strong soft skills to the table. One organisation interviewed as part of our Data science in WA report, who had a number of entry-level data scientists, planned to focus “Less on the technical and analytical side, more on domain knowledge and communication skills”. They highlighted that most of their data scientists were straight from university and often lacked the soft skills needed to be successful within their organisation.

Believe it or not, communication is key in data science. Data scientists need to be able to communicate across many formats. This applies in writing, speaking, and listening, but most importantly, the ability to transform the data into a transparent solution to people within the organisation. The value of a data scientist being able to clearly communicate their results was emphasised by several stakeholders interviewed for our recent Data science in WA report. It’s all well and good to be able to develop a data-driven solution for your organisation, but if you can’t communicate your results to your team in business-terms, then you can’t showcase the true value of your findings.

Alongside, and in conjunction with communication, domain knowledge could be one of the most important abilities for a data scientist to have. Understanding the organisation, or the field, that your data comes from allows for more precise problem-definition, and in turn a more accurate data-driven solution. Stakeholders interviewed for our Data science in WA report recognised that some industry’s data scientists required a much deeper understanding of domain knowledge, particularly in the healthcare sector. And whilst data scientists are in no way expected to be domain experts, the ability to communicate with people across the business in order to understand the key aspects of the operation is critical. Our stakeholder’s claimed that their data scientists “need to be able to work closely with subject matter experts and quickly understand key concepts”.

 

  1. Creative Problem-Solving

Organisations often struggle to efficiently solve business challenges because their focus tends to be solution-oriented, rather than breaking down and properly identifying the problem. As a result, there is a demand for graduates who excel in, or have experience utilising a creative problem-solving approach.

Design thinking is one style of creative problem-solving that is proving to be extremely beneficial in improving data science project outcomes, as it provides a solution-based approach to solving difficult problems. In this collaborative process we seek to understand the user, challenge assumptions, and redefine problems in such a way as to identify alternative strategies and solutions that might not be instantly apparent.

WADSIH follows the Innovation Central Perth Design Thinking Model:

 

WADSIH has been working closely with a number of WA organisations to help them better understand their problem in order to find the most effective data science driven solution. By following this model, our design thinking sessions give organisations a hands-on approach to distilling the right problem into a coherent and insightful statement and translating ideas into definable concepts. Don’t hesitate to contact us if your organisation could benefit from some creative problem solving.

Whether you’re academically trained, or upskilling your way into the role, knowing what employers want will help you land your ideal data science position. Keep these 3 skill areas in mind and you’ll be filling an identified skills gap in Western Australia. And if you ever need any advice, or have any questions, don’t hesitate to get in touch with us here at the hub.

Alicia Fairbank

 

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