Is 35 too old for data science? – Data Science Nanded

Seeking new challenges in data science and data analytics at the age of 35 may seem to be an intimidating career option for you. It is natural to be concerned about beginning a career option from scratch or upscaling yourself to a new career in data science at age 35. On top of that, many opine that tech domains are a good career option for younger people. Ageism is a known problem in almost every tech industry. Tech workers and professionals over 35 are considered too old to survive in the data industry.

Conversely, experienced candidates are highly valued in the analytic data field. A recent study conducted by Zippia showed that data science professionals, on average, in the United States, are 43 years old despite the existence of the tech industry ageism concept. Therefore, experience in the field of data science is one of the most sought-after skills recruiters and hiring managers look for in data science candidates.

If you think 35 is too old to start a career in the data field since then, the answer is no. Individuals can begin a career in data science and data analytics, like tech domains, later in their career life as long as they have the passion and knack for it. Professionals aged 35 will be evaluated based on their experience, skill sets, and current qualifications. Any individual can become a data scientist at any age.

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What are the benefits of making a midlife career in data science?

People aged 35+ can witness tangible benefits of making their career in the data science domain, such as the following

More years of experience

Although ageism and misconceptions are present in many domains or industries, not all companies are looking forward to hiring a 22-year-old data scientist or data analyst with a degree from Harvard University. Applicants in their middle age, between the age limits of 35 to 40 years, can bring many valuable benefits to businesses and companies because of their in-depth knowledge and skill set that puts them ahead of the younger generation. A 35-year data science professional stays ahead of his or her younger competition in terms of technical skills, professional networks, and industry insights that the person has built over the course of years. Experience is a significant factor in the field of data science that most hiring managers across diverse domains are looking for other than a technical qualification.

Better leadership and communication skills

A professional aged 35 years can make a career in data science as long as they have good writing, leadership, and communication skills. Some of the technical skills that are mandatory for data scientists are mathematics and programming knowledge. However, soft skills remain the most underrated data science skills that hiring managers and recruiters seek in candidates. Data scientists must carry out all the technical aspects of their job role and communicate their data findings. From applications to cover letters to emails and presentations, people in their mid-careers tend to have better communication, writing, and leadership skills because of their many years of experience. These powerful skills make them stay ahead of their younger competition. As these critical skills cannot be developed quickly, more experienced data scientists can help their application set itself apart from the rest. Professionals can also directly integrate their skills into a hybrid role, making them a true asset to the organization.

Shortage of skilled professionals for senior data science roles

Becoming a fully-fledged senior data science professional takes years of experience for a young data scientist. To grab this position, they have to spend many years bringing perfection in their technical skills and work experience from domain expertise and acquire additional qualifications and skill sets. However, people in their mid-career have prior experience in data science and data analytics and can quickly fill the position of senior data science roles in companies and organizations. Therefore, if you are 35 or more than this age limit, then a data science career is the correct route for you to pursue, as your experience will put you far ahead of the competition.

What are the challenges of making a career in the data science domain at 35?

Although a mid-career in the field of data science can prove beneficial for many professionals aged between 35 to 40 years, they are experienced. As a result, they can fill senior data science job roles quickly. But, in particular areas, it can be complex for them to break into the new and evolving data science industry. Some challenges that people in their mid-career may face in data science, like evolving domains, are as follows.

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Staying updated on the latest data science trends and technology

Data science and data analytics are fast-evolving domains, and with each passing day latest innovations, tools, and changes are coming up. This challenge can be overcome easily by undergoing data science certification courses, reading blogs, listening to data science podcasts, and enrolling in data science course programs. However, people in their mid-career often find it challenging to get trained on the latest trends or stay relevant in the market, unlike their younger peers.

Connecting with younger peers

For a person in his/her mid-career, entering the data industry comprising the majority of younger people can be a potentially intimidating and frustrating experience. It is pretty challenging for someone in their mid-career to connect or communicate with their young peers, especially if they have young bosses to give them commands or instructions. This type of challenge can be easily overcome by starting conversations with younger people in the industry. The main aim is to connect with young peers and colleagues and cultivate cordial relationships.

How to launch a later science career at the age of 35 years?

When filling the role of a data scientist at age 35, recruiters and hiring managers will evaluate your skillsets, experience, and knowledge to understand whether you are the right fit for the company. You must create a portfolio with solid case studies, highlight your technical skills, problem-solving aptitude, and ability to handle massive datasets and extract meaningful and actionable insights. You can also enroll for data science certification courses where you can hone your foundation, acquire an understanding of data science and learn hands on experience to become job ready.

Conclusion

It is never too late to begin a data science journey. Mid-career change can sometimes be complex and intimidating; however, you can become a data scientist at any age. The profession of data science is welcoming to talented and analytical minds with the right skills and knowledge base.

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