(Photo by NICOLAS ASFOURI/AFP via Getty Images)
key takeaways
- AI is here to stay, and it likely means there will be some changes in the world of work. Some jobs will change and some will be lost entirely.
- However, as the AI industry explodes, there are a number of roles that will go from marginal to hugely popular, and others that will be entirely new.
- Regardless of whether you’re a tech-focused data scientist or more of a sales and marketing guy or gal, we’ve got a list of AI jobs that are sure to have something up their street.
If all the fuss around ChatGPT, Dall-E, Tesla’s fully autonomous driving mode, and *ahem* Q.ai has shown us anything, it’s that artificial intelligence is here to stay. The knee-jerk reaction of many old-fashioned meat machines—sorry, humans—is to worry about what this means to your income.
We’ve been told for years how AI is going to take care of our jobs, and it’s true that in many industries, machines, robots, and other technologies have drastically reduced the number of workers.
That being said, many of the jobs AI has done so far are often seen as dangerous, repetitive, and boring. Not too many people get great job satisfaction turning the same 5 screws on a production line for 40 hours a week.
But a machine? Do not care
So yes, we will continue to see changes in the workforce as AI innovations help experts do better jobs and eliminate some of the most basic and fundamental roles in many different industries.
Even better, AI is also going to create a lot of jobs. We’re already seeing the industry explode, and below we’ve covered some of the best jobs to consider if you’re looking to land a job in this fast-growing industry.
Download Q.ai today to access AI-powered investment strategies.
AI research scientist
Let’s start from the beginning. An AI research scientist conducts research in the field of artificial intelligence and machine learning, with the goal of developing new methods and algorithms that can be used to solve a wide range of problems. They typically go to work in places like universities, research institutes, or in the R&D departments of large tech companies, and are at the forefront of developing new AI technologies in the early stages. Not only that, but they also consider all the issues related to the use of AI. These research projects can take years to complete and require significant resources (ie cash). Your research may include areas such as the industries most likely to be affected by AI use, ethics, and even the environmental consequences of widespread AI use. The main goal of the AI research scientist is to produce new knowledge and drive the state of the art in the field, rather than to solve specific business problems.
The job of an AI research scientist covers a ton of ground that requires expertise in computer science, mathematics, and statistics, as well as the ability to think critically and creatively.
AI data scientist
An AI data scientist goes a step beyond the purely theoretical nature of the research scientist and moves towards the practical applications of AI technology and theory. They use their AI and machine learning skills to analyze and interpret complex data sets, often with the goal of uncovering valuable insights and building predictive models. Basically, they find practical applications for the high theory of the researchers.
AI data scientists often work on projects related to natural language processing (NLP, like ChatGPT), computer vision (like Tesla’s autonomous driving mode), or voice recognition (Siri and Alexa).
They also fit complex models so that they can learn from the data and make predictions or decisions without being explicitly programmed to do so.
AI data scientists need to know a lot about machine learning algorithms and techniques, and they need to be able to work with large and complex data sets. We’re not talking about a couple of Excel spreadsheets here.
machine learning engineer
Continuing the process, we are getting closer to actual AI products and services that can be used by consumers. That’s what a machine learning engineer does.
They who apply their knowledge of machine learning and software engineering to design, develop and implement systems that can learn from data and improve their performance over time.
Machine learning engineers often work closely with data scientists to bring machine learning models from research to production. The data scientists give them the algorithms, and the engineers put those algorithms into an actual product.
Often, they also need to integrate their models with existing software systems, which requires a strong understanding of software engineering best practices and a deep understanding of the implementation platform and infrastructures.
Continuing with the example of Tesla’s autonomous driving mode, the data scientist will create the program with which the AI system can classify its training history and recognize patterns.
It is this algorithm that will enable the command that says “This is a car, when you see one die in front of you, hit the brakes.” The engineer will work to implement this algorithm in a Tesla so that it works as intended and in conjunction with all other technology in the car.
The role of a machine learning engineer is typically broader than the role of a data scientist. His main focus is taking the model and preparing it for production and making sure it performs well and is scalable.
AI product manager
If you’re not a tech person, don’t worry. There are AI roles for you too! An AI product manager is less of a scientist or engineer and more of a sales and marketing person.
They are responsible for leading the development and launch of AI-based products and services. They must understand customer needs and market trends, set product strategy, and work with cross-functional teams to bring an AI product to market.
An AI product manager must have a strong understanding of AI and machine learning concepts, as well as knowledge of the industry and market where the product will be used. That said, they don’t understand exactly how the technology works, just what it’s capable of.
AI product managers may work in various settings, such as technology companies, start-ups or consulting companies, or different industry verticals. They work closely with data scientists, engineers, and other stakeholders to ensure the product is successfully developed and launched.
An AI product manager must have the ability to balance technical knowledge, market understanding, customer needs, and business goals to create and market a successful product.
AI consultant
An AI consultant is someone who helps non-AI companies and organizations see how they could implement AI in their business.
An AI consultant must have a solid understanding of the field of AI and machine learning, including the technologies and platforms available. They must also have experience in a specific industry or domain and understand how AI can be applied to address business challenges in that area. That is why many consultants will specialize in particular fields, such as health or agriculture.
This is another role that does not necessarily require a deep technical and scientific understanding of AI algorithms and machine learning. Instead, you could get by with a broad knowledge base about the AI products and services available within certain industries, what they do, and how they work.
How you can get an AI investment assistant
If you’re convinced that AI is the future, you might want to take advantage of it as your own assistant in your day-to-day life. If you already have an Alexa, a Ring Doorbell, a Roomba (wow, Amazon has really gone all in on AI, huh?) and you’re looking for your next AI helper, what about your investment portfolio?
At Q.ai, we use the power of AI to give investors access to cutting-edge strategies that are typically only reserved for high-flying hedge fund clients.
Examples include our Emerging Technology Kitwhich uses AI to predict performance across four technology verticals and then automatically rebalances each week based on those projections.
Or the forbes kitwhich leverages our relationship with Forbes and uses its proprietary data and natural language processing to find relationships between trending companies, their overall sentiment, and their stock prices.
Not only that, but in our Foundation Kits we also offer portfolio protection. This uses AI to analyze your portfolio’s sensitivity to a variety of different risks, such as interest rate risk and volatility risk, and then automatically implements sophisticated hedging strategies to help protect against them.
Like we said, it’s cutting edge stuff and it’s available to everyone.
Download Q.ai today to access AI-powered investment strategies.
