10 AI skills needed for a career in technology
Now it is no secret that artificial intelligence (AI) proficiency is the need to advance in the high-tech job market. Advances in technology and new platforms are unfolding seemingly every day, with some of the world's largest brands looking into the best implementation of AI.
If you're interested in where AI is heading, take a look at these statistics.
There is still a lot of growth left to achieve in AI. But one of the most eye-opening numbers is that 88% of people who don't use generative AI are unclear as to how it affects their lives. This provides a great opportunity to learn AI skills that will help boost your tech career.
What exactly are those skills?
Why is AI in demand?
Tech experts and major publications say AI shows no signs of slowing down. In fact, according to Bloomberg Intelligence, the AI market size is expected to grow more than 30 times over the next decade.
Given these growth numbers, some of the most sought-after skills in the job market today are related to AI for many reasons.
First, artificial intelligence skills allow businesses to meet current customer service. The digital age is creating smart customers who know what they want and when they want it. As a result, businesses need to make each customer feel personal about their buying journey. Traditionally, quitting it requires a lot of company time and resources. However, AI allows businesses to customize their experiences (among other tools, such as data-driven insights and AI chatbots).
Naturally, businesses can also use AI to automate data management. The main advantage of this use case is that it reduces human error when processing large data sets. This makes businesses across all industries run more smoothly, but is an essential advantage in sectors where highly sensitive data such as credit scores and bank account information are processed daily.
These trends open up tremendous growth opportunities for technology itself, but people with high-demand AI skills still have a place in the job market. Today's AI platforms can do amazing things, but such complex technologies require guidance. Those responsible for monitoring and managing these machines require highly specialized skills and education.
Essential AI skills
So, if AI requires humans and these humans need highly skilled, the question is, “What specific skills do you need?” Let's explore some of these skills.
Technical Skills:
- Programming Language: Python is the best, but R, Java and C++ are also important based on your work. You need to be proficient in writing clean and efficient code to build and interact with AI models.
- Data expertise: AI is all about data. Understanding data modeling, warehouses and processing is essential. Being familiar with big data concepts and tools is a plus.
- Machine Learning: This is the core of AI. It is important to understand machine learning algorithms, such as deep learning and reinforcement learning.
- AI and ML frameworks: Tensorflow, Pytorch, Scikit-Learn – these are tools that can be useful for building and training models. Proficiency in these frameworks is a major asset.
- AI Deployment and DevOps: Once you have built your model, you need to deploy it in the real world. Understanding deployment techniques and DevOps principles is becoming increasingly important.
- AI Security: AI systems are vulnerable to attacks. Knowledge about AI security threats and mitigation strategies is a valuable skill.
Non-technical skills:
- Communication and collaboration: AI projects are rarely a solo effort. Complex AI concepts must be explained to non-technical stakeholders and effectively collaborated with team members from various backgrounds.
- Adaptability and continuous learning: The AI field is constantly evolving. It is essential to learn and adapt new tools and techniques.
- Critical Thinking and Problem Solving: AI is about solving complex problems. Strong analytical and problem-solving skills are important.
- Domain knowledge: Although not necessarily, having a deep knowledge of a particular industry (healthcare, finance, etc.) makes you a very popular AI expert.
The specific combination of skills required depends on the career path of the AI you choose. However, this should help provide a strong foundation for success in the exciting world of AI.
How to build AI skills
Job seekers should consider the following ways to develop marketable AI skills:
- Internship: An internship is an opportunity to gain practical, real-world experience with impactful AI projects. You can explore the practical implications of modern platforms and build a portfolio of work. Some companies may even offer apprenticeship programs that combine internship experience with a structured curriculum of educational courses.
- Online Course: Many companies, including some leaders in the tech space, offer online resources to teach in and out. Students can choose subjects ranging from data science to machine learning (ML). The courses often use a self-study format. This is perfect for learning at your own pace. However, additional learning may be required without the guidance of experts and instructors.
- Bootcamp: Think of bootcamp as a more intensive internship. By working on real projects and acquiring the skills needed for the job market in a shorter time frame, you can gain knowledge of the AI space. Some people have a higher placement rate than others, so take a thorough investigation into the boot camp.
- University degree: When you choose a more structured curriculum, the degree to which you receive is likely to be focused on computer science, mathematics, or data science, limiting the amount of time and experience in real AI applications.
The best job with AI right now
Designing a long-term career with AI means choosing which path in the AI ecosystem you choose to follow. Many job titles available today are looking for AI skills in demand.
Here are some top positions to look for. Unless otherwise stated, the salaries listed below are in accordance with Simplilearn.
1. Machine Learning Engineer
Machine learning (ML) or AI engineers are responsible for end-to-end management of AI systems. They analyse large datasets and play an integral role in designing, building and managing AI models that continue to improve and grow through experience.
Qualifications: This position typically requires a degree in statistics, computer science, or related fields. Additionally, you need to build proficiency in programming languages such as Python, R, and Java. Potential ML engineers can also gain previous experience using machine learning algorithms.
2. Robot Scientist
Robot scientists are responsible for building robots that emulate human intelligence. These tasks range from assembling the product to handling hazardous chemicals, or assisting with accurate medical procedures such as surgery.
Qualifications: Robotic scientists usually require a degree in mechanical, electrical, or robotics. This role also requires a complex knowledge of superior system engineering, robotics hardware and computer programming.
3. Data Scientist
Data scientists or data engineers focus on finding patterns and trends in large quantities of raw data. They rely on algorithms and statistical tools for their work, and companies use data scientists' findings to make data-driven decisions and drive their businesses forward.
Qualifications: Typically, to become a data scientist, you need a degree in data science, computer science, statistics, or related fields. Proficiency in programming languages like Python and R, as well as data analysis tools like Tableau and SQL are pluses.
4. Software Engineer
Software engineers in the AI space develop software applications that utilize artificial intelligence or machine learning. These platforms can help your business.
- Identify cost-effectiveness that allows investments in other areas of potential growth
- Streamline your development workflow and decision-making
Qualifications: A degree in statistics, mathematics, computer science, or related fields is required. It is also important to be highly familiar with programming languages, AI or ML algorithms, and data analysis.
5. AI Product Manager
AI Product Managers offer more monitoring capabilities to monitor the teams who produce AI products, from ideas to deployment. Ideally, these individuals have complex knowledge of the AI market. They understand industry regulations, technical challenges, and other hurdles that other hurdles production teams may face on their journey to the market.
Qualifications: Filling these positions requires a deeper understanding of both product management and AI technology. Additional technical background is a bonus.
Are you interested in technical work at Intuit?
Using these AI skills will not only boost your career, but also increase your current marketability. As this technology grows, these skills will only grow in demand. You spend time learning them and the transition to today's AI job market will be smoother. At Intuit, our strategy is to build an AI-driven technology platform to help small businesses thrive. If you're interested in being part of our team, please visit our Intuit Careers page for all AI openings and other technology roles. Learn how Intuit Technologists leverage AI to solve and make meaningful impacts on some of the world's most challenging financial problems.
