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5 Ways for Data Scientists to Keep Coding and Growing

By Vahid Haghzare, Director Silicon Valley Associates Recruitment &
Armae Garcia, Marketing Associate, Silicon Valley Associates Recruitment

We can say with confidence, as a professional IT Recruitment Agency in Hong Kong that folks who pursue a career in data often actually begin as coders, spending considerable time typing. And as you gain experience, you will develop other useful skills in business, and reporting and moreover, you’ll learn to collaborate with stakeholders. These skills can be compared with managerial skills. And this is where it all comes down.

For Data Scientists whose interest is in the hands-on work of coding and interacting directly with data, the progression from an individual contributor role to a more managerial role can be frustrating. Yet, some people can keep their hands-on work and still advance their careers.

At Silicon Valley Associates Recruitment, we encourage our Data Scientists to not let the need to grow professionally hold them back from continuing to code! As Data Scientists try to advance in their careers, whether you are in Hong Kong, Shanghai, Singapore, Japan, and Dubai, you can employ these strategies below to stay in touch with your coding roots, and not lose ground on the basic skills which are the foundation of your career.

1. Find other means to move forward with your career

Although many people advance in their careers by moving into management, it is not the only way to advance. For developers who want to stay involved in coding and data, it may be advantageous to explore career paths that offer opportunities for advancement into other IT sectors.

Those who pursue such a path can expect to refine their technical expertise and gain specialized knowledge. These individuals often spend more of their time on creative endeavors, than they do on usual management tasks.

Data scientists screened at SVA Recruitment tell us every day that their day consists of 20 percent to 30 percent reviewing projects done by others on the team, and 30 percent to 40 percent time for innovative projects they are leading. For the remaining time, they spent it working on strategy, such as figuring out how to make data science scalable.

Just as you need a top-notch surgeon to perform surgery, you need a highly trained data scientist to analyze your data. It requires skill in strategic planning and the ability to manage daily business operations.

Although responsibilities as a junior data scientist have changed, the job still includes coding. It would be inappropriate to continue in the technical track if you don't code.

A Data Scientist finds it rewarding and stimulating to answer business questions with data and mathematics - and that’s what attracts them to the field.

A growing number of companies are telling IT Recruitment Agencies in Hong Kong that they are expanding their opportunities to include jobs beyond the usual management track. Companies have begun expanding in order to attract a more diverse range of talent in the booming data science market. Data science also has many frontiers, often requiring a great deal of time and specialized knowledge.

See Also: What trends to look forward to in 2022 as a Data Scientist

2. If there is no way, create one.

Yes, more companies are offering technical tracks for a data science career advancement; however, not every company does.

Our clients from Hong Kong, Shanghai, Singapore, Japan, and Dubai suggest those who want to specialize in a domain go after the one path that leads to the same goal.

Taking a specialization requires commitment and focus, which means you need to spend time on one subject rather than trying to learn ten different skills. Start by picking a domain where you want to focus your efforts.

As an IT Recruitment Agency in Hong Kong, we suggest that there are many different technical areas in which data scientists might choose to specialize—such as natural language processing, computer vision, and A/B testing—but they also might choose to work in business sectors such as finance or advertising.

When you create a career plan, you should look for mentors who can help you reach your goals and work with your manager to identify projects that will lead you toward your specialization.

In some fields, you can become an expert in a matter of months. In other fields, years of experience are required to rise to the top. Technical or not, you will be valued by your company, and you are likely to grow professionally.

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3. Better to stay than start over at another company

Specialization can be difficult if you move from place to place frequently.
An average data scientist stays in their job for less than two years, according to our IT Recruiters at SVA Recruitment. It is partly due to a large number of job opportunities available in this field. When a person is constantly switching from one job to the next, he or she does not build expertise in any one field. We recommend that those who want to keep coding as they advance their careers stay with their current employers.

Technical skills and seniority are the most obvious benefits of staying long in a job. But accumulating institutional knowledge with an employer can be just as important. Institutional knowledge will enable you to retain a hands-on approach.

For instance, as a senior data scientist on a team, spend less time coding, but still provide input on the projects my team is working on.

Institutional knowledge has helped to retain a detailed understanding of client data and the work of team members. They are able to correct mistakes and provide constructive feedback to team members.

A senior data scientist is able to be hands-on with his/her current company’s coding and data, but she would not necessarily be able to do so at another company because he/she would have to build another institutional knowledge. If she moved to another company, it might not allow her the same hands-on role she could have as a senior employee at her current employer.

See Also: How to Start Off your Career in Data Science now

4. Start Small

Specialization is not a viable career path for everyone. You may be able to gain skills in data science without leaving your current job by finding a startup, small company, or small team where you are required to do the hands-on work of data science. They can be important opportunities for growth, but if you reach the top, you must strive to master a variety of skills.

Working with small teams can be difficult, as it means fewer people to help with data analytics and coding. As a senior contributor, you'll be responsible for data and model analysis and managing a small team.

One of SVA Recruitment’s clients in Hong Kong shared that their company originally did not have a budget for data science and data analytics, so they were initially unable to hire more data scientists. He was thrilled with the idea of being able to handle every aspect of the data management process. With the success of the company, the data science team—while small—has continued to grow. In addition to leading a team, he continues to code as well.

As an example, International freight charges and shipping time can be major issues for companies. They use data to help solve those problems, maintaining a profit-making environment for the company. While he works in a cross-functional data science team now, opportunities to explore different aspects of data science still exist.

5. Mentorship

Data scientists cannot avoid becoming more senior without assuming some management responsibilities. In those instances, mentoring junior data scientists is a good way to stay on top of your coding and data skills.

Before models are deployed, ensure that you’ve peer-reviewed team code.
Encourage your team to spend up to two hours per week on development - learning new skills and sharing them with the team) and to share their accomplishments with the rest of the group every month or quarter.

Technical mentoring can occur when a Data Scientist works through the science reviews of ongoing data science projects, but also when the leaders of those projects encounter challenges. To be successful in being a coach and a player, one must work hands-on with the coding and data. But it is important to mentor and also keep your hand in data science.

Mentoring can help you maintain a continuity of coding and direct data work, but you have to stay active for your mentoring relationships to be effective. If you stop coding for a while, your coding skills will become outdated. Once you stop coding, the authority of your insights diminishes because you are unable to back them up with evidence, others may not support your authority - this is why you need to keep coding.

See Also: Mentorship, The way forward for Data Scientist

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Silicon Valley Associates is ideally positioned to support the continual demand from tech companies and IT Departments looking to hire in Hong Kong, Asia, and Worldwide. Please let us know if you would further advise on the above topic or your hiring needs