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What Makes a Data Scientist Stand Out?

There is a growing obsession with hard skills, but soft skills can make you stand out from the crowd.

Photo by Mitul Grover on Unsplash
Photo by Mitul Grover on Unsplash

The number of Data Scientists continues to grow every year, and the markets seem to accommodate every one of us. Luckily, there is no sign of slowing down. According to the US Bureau of Labour Statistics, the need to manipulate data will roughly rise to 11.5 million job openings by 2026 [1]. Also, Data Scientists salaries have reached an average of $120,000 in the US [2].

However, the competition for certain tech-industry positions is exceptionally high, and it looks like it’s going to get tighter. There are many bootcamp releasing ‘batches’ of Data Scientists and recent graduates from multiple universities seeking for jobs, let alone those in a career transition. Also, the global pandemic has shown companies that we can all work remotely, making it easier to hire Data Scientists from other countries.

Every Data Scientist will have a GitHub showcasing their brilliant work and analytical skills in a few years. The vast majority of data-professionals will be fluent in Python, R, SQL and maybe some software engineering skills. Therefore, having an arsenal of techniques and skills to tackle data problems will not make you stand out from a large number of Data Scientists. So, here are the characteristics that can, and most certainly will set you apart from the crowd when job hunting.

1. Improve Your Soft Skills

Soft skills refer to character traits and behaviours that labels a person’s ability to interact with other people effectively [3]. In most professional environments, soft skills complement hard skills – a person’s knowledge and technical skills, such as programming in Python and math.

Communication is not only a soft skill, but it is, arguably, the most relevant skill to help one succeed professionally. Ironically, excellent communicators are active listeners. By listening carefully and paying attention when speaking to other people, you show care and have a better chance of understanding their problem. In doing so, Data Scientists will solve problems more efficiently. As a result, Data Scientists who communicate effectively are more likely to reach senior management positions in a company.

No matter how good you are, data alone doesn’t mean anything. You need to communicate your results in a way that assists in decision making. All of the theoretical knowledge you have pales compared to someone’s ability to explain how a simple machine learning model works and what its results mean to their target audience. Data Scientists could accept the importance of effective communication skills, and tailor their language to each audience. Potentially, your ability to communicate with non-tech professionals will make you a more effective problem-solver (and will make you a pleasure to work with). Thus, standing out from the crowd.

2. Good Enough, Not Perfect

Do not let ‘perfect’ get in the way of ‘good enough.’ This is one of the most significant heartburn sources with any Data Science graduate student. In academic environments, aspiring Data Scientists are expected to work until perfection. It is not a surprise they feel compelled to get it perfect all the time. However, companies do not run on ‘perfection.’ Business executives develop strategies to adapt to the market and beat the competition, not to become the perfect company. As a Data Scientist who is part of a particular company, you should mirror its pace and mindset, rather than seek academic perfection.

From a senior executive position, extracting an extra 2% lift from your machine learning algorithm will make little, if any, difference to the business. Undoubtedly, many machine learning professionals could increase their algorithms predictions, but at what (financial) cost? Marginal gains usually do not pay-off if companies lose decision-making speed and commercial flexibility. So, when good enough has provided tangible results, stop and move on to the next project. ‘Let it go.’

Here are a few tips on how Data Scientists can flex that muscle:

  • Develop personal projects that are interesting to you and prioritise reaching answers quickly. By creating tight deadlines, there is a good chance your answers will not be perfect.
  • Freelancing/volunteering: being in a real work environment is where you will see how people care very little about some of the things that academics care about a lot. You will most likely feel uncomfortable initially, but that is the point.
Photo by Emilio Takas on Unsplash
Photo by Emilio Takas on Unsplash

3. Business-oriented

Suppose you have decided to work for the industry, rather than academia. In that case, developing your commercial and business skill is a must. Data Science has been moving towards practical integrated tech industry solutions, such as good enough quality at high performance and low cost per predictions.

In a company, Data Scientists have to bring real business value out of the data. Some professionals might not agree, but bluntly speaking data-professionals need to be able to increase company profits using data and their insights. Otherwise, senior stakeholders ares merely wasting resources with you. The more you contribute to the bottom line, the better. Therefore, Data Scientist who manage to become more business-oriented will stand out from other professionals who have an academic-like mindset in a commercial environment.

Ask yourself some of these questions:

  • What is the business trying to achieve?
  • How does your model or your project help accomplish the company’s goals? Can you see the big picture?
  • Do you know the terminology and the general marketing strategies of the company your work for (or wants to work)? How can you get more involved in the business? Even if indirectly.
  • Can someone in a different department provide you with a quick induction session about brands and commercial views?

Hopefully, these questions will get you to click and become more business-oriented. You will be surprised at how future projects will become easier to develop once you are all on the same page.

Conclusion

The corporate Data Scientist roles will inevitably undergo some changes. Because of the high number of professionals migrating towards data business, the competition among candidates will be tighter than ever. How can you stand out? Well, honing your business understanding is the first step to create and thrive in any profit-driven company. However, it is vital to understand that aiming for perfect results is unrealistic in a fast-paced (sometimes volatile) business environment, as competitors move fast. Therefore, business strategies have to respond accordingly, which rarely meets the timing for reaching perfection. Finally, it is nearly impossible to achieve big goals in a company without relying on seamless communication. So , why not start thinking of your communication skills in the way you think of your hard skills?

Thanks for reading. Here are some articles you will like it:

Trends in Data Science That Will Change Business Strategies

Why data scientists and business executives struggle to work together

Best Cities to Work as a Data Scientist


References:

[1] Forbeshttps://www.forbes.com/sites/louiscolumbus/2019/01/23/data-scientist-leads-50-best-jobs-in-america-for-2019-according-to-glassdoor/?sh=6d4e71e77474

[2] Business Insiderhttps://www.businessinsider.com/data-scientist-best-job-in-us-right-now-2018-2?r=US&IR=T

[3] Soft Skills – https://www.investopedia.com/terms/s/soft-skills.asp


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