Artificial intelligence (AI) has moved from being a niche topic to a dominant technology. With rapid advancements, AI has been creating massive business opportunities and breakthroughs, prompting leaders to invest more in it. With an increased demand for AI capabilities, the demand for skilled individuals working in this field has also skyrocketed. If someone wants to dive into the field of AI and data science, this would be the best time to do so. A career in AI is rewarding and exciting, and the field is full of opportunities. This article could be a good guide to people looking to build a career in this field.
Find your niche
Like every career, there are different roles in the workplace, and it’s important to familiarise oneself with them. In AI parlance – we have roles starting from business translators, data engineers, and data scientists to BI developers, Model Ops engineers, and project managers, etc. Depending on your area of interest, you can build your career as you move along. Careers are no longer narrowly defined by a specific title and a set of skills tied to it.
In the ever-changing environment, having a T-shaped career can make it easier to adapt. Used in-house at McKinsey & Company for the first time, this is also sometimes referred to as a “Generalised Specialist” profile. The vertical bar on the T-shaped person depicts an individual’s unique abilities and how deep their knowledge is. The horizontal bar shows the person’s ability to use the available skills and capabilities to collaborate with others in different areas of expertise.
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Understanding what companies want
There are three dimensions to what enterprises generally look for in an AI professional – mindset, skillset, and toolset. The mindset translates the ability to understand the requirement and converts it into a problem-solving framework. This is essential to understand the business context or the domain part of it. Next is the skillset, which focuses on the primary skillset and explores your horizontal capabilities to find the best solution. If you can think in terms of where you can utilise a part of the solution to address another different issue that has similarities in nature, you then create unique, anomalistic solutions. And the last part is the “Tool Set”, or the technology part, which helps in translating the thought process into a realisable solution – the application part. How you capture all three pieces ultimately determines your success as an AI professional.
Be adaptable to different technologies
Technology is advancing at a rapid pace; leveraging new technology saves time and money while improving final output results. Everyone learns and consumes information differently; here are some of the best ways to start learning and keeping up with technology:
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- Leveraging company resources
- Learning through open source
- Online training
- Virtual events and webinars
- Collaborating with your peers
Conventional learning alone will not suffice. There is a need to accelerate learning and skills development in data science and AI and make it available to a large number of aspiring learners.
Data science and AI have become all-pervasive across functions and industries. Up to 90 per cent of small, mid-size and large organisations have developed advanced analytics capabilities to stay relevant in the market. They use these capabilities to build analysis models, simulate scenarios, and predict future trends. AI and data science-related jobs will continue to grow in the coming years.
This article offers broad insights on what to look for in a career in AI; it’s time to make that move and get started on your learning curve. One can only hope to progress in a career if there is constant value addition. The key is to be confident in your skillset, communication and networking skills, and being a team player.
This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill the form here.