- AI is here to stay, and it’s likely to mean there are going to be some changes to the world of work. Some jobs will change, and others will be lost altogether.
- However, as the AI industry explodes, there are a number of roles that will go from the fringes to widely popular, and others that are going to be totally new.
- Regardless of whether you’re a tech focused data scientist or more a sales and marketing kinda guy or gal, we’ve got a list of AI jobs that’s sure to have something right up your street.
If all the hype around ChatGPT, Dall-E, Tesla’s Fully Self Driving mode and *ahem* Q.ai, has shown us anything, it’s that artificial intelligence is here to stay. The knee jerk reaction from many old fashioned meat machines, sorry, humans, is a concern around what this means for their income.
For years now, we’ve been told how AI is going to take our jobs, and it’s true that in many industries, machines, robots and other technology have cut workforce numbers dramatically.
With that said, many of the jobs being taken by AI so far are often considered dangerous, repetitive and boring. There aren’t too many people out there who are going to get great job satisfaction from turning the same 5 screws on a production line for 40 hours a week.
But a machine? They don’t care.
So yes, we’re going to continue to see the workforce change as AI innovations help experts do better work, and cut out some of the more basic and fundamental roles in lots of different industries.
Even better, AI is going to create a heck of a lot of jobs too. We’re already seeing the industry explode, and below we’ve covered some of the best jobs to consider, if you’re looking to get into a job in this fast growing industry.
Download Q.ai today for access to AI-powered investment strategies.
AI research scientist
Let's start at 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’re usually going to work in places like universities, research institutes or in the R&D departments of large tech firms, and they’re at the forefront of developing new AI technologies at the earliest stages. Not only that, but they also consider all of the issues surrounding the use of AI.These research projects can take years to complete, and require significant resources (i.e. cash money).Their research can include areas like the industries that are most likely to be impacted by the use of AI, ethics and even the environmental consequences of widespread AI use.The primary focus for the AI Research scientist is to produce new knowledge and push the state of the art in the field, rather than solving specific business problems.
The work of an AI research scientist covers a ton of ground requiring 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 further from the purely theoretical nature of the Research Scientist, and moves towards practical applications of the AI technology and theory. They use their skills in artificial intelligence and machine learning to analyze and interpret complex data sets, often with the goal of discovering valuable insights and building predictive models.Basically, they find practical applications for the high theory from the researchers.
AI data scientists often work on projects that are related to natural language processing (NLP, like ChatGPT), computer vision (like Tesla’s self-driving mode), or speech recognition (Siri and Alexa).
They also fine tune complex models to allow them to learn from 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 down the process, we’re getting closer towards actual AI products and services that can be used by consumers. That’s what a machine learning engineer does.
Theywho apply their knowledge of machine learning and software engineering to design, develop and deploy 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 the research phase into production. Data scientists give them the algorithms, and engineers put those algorithms into an actual product.
They also often 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 deployment platform and infrastructures.
Sticking with the example of Tesla’s self-driving mode, the data scientist will create the program with which the AI system is able to sort through its training history and recognise patterns.
It’s this algorithm that will allow the command that says “This is a car, when you see one run out in front of you - hit the brakes.” The engineer will work to implement this algorithm into a Tesla, so that it works as intended and in conjunction with all the other tech in the car.
The role of a Machine Learning Engineer is often broader than the role of a data scientist. Their primary focus is to take the model and make it ready for production and make sure it's performant and scalable.
AI product manager
If you’re not a tech person, don’t worry. There are roles in AI for you too! An AI product manager is less of a scientist or engineer, and more of a sales and marketing person.
They’re responsible for leading the development and launch of AI-based products and services. They have to understand customer needs and market trends, setting product strategy and working with cross-functional teams to bring an AI product to market.
An AI product manager should have a solid understanding of AI and machine learning concepts, as well as knowledge of the industry and market where the product will be used. With that said, they don’t understand exactly how the tech works, simply what it’s capable of.
AI product managers may work in various settings such as technology companies, startup or consulting firms, or different industry verticals. They work closely with data scientists, engineers and other stakeholders to ensure that the product is developed and launched successfully.
An AI product manager needs to have the ability to balance technical knowledge, market understanding, customer needs and business objectives to create and market a a successful product.
An AI consultant is someone who helps non-AI companies and organizations see how they could implement AI into their business..
An AI consultant needs to have a strong understanding of the field of AI and machine learning, including the available technologies and platforms. They should also have expertise in a specific industry or domain, and understand how AI can be applied to address business challenges in that area. That’s why many consultants will specialize in particular fields, such as healthcare or agriculture.
This is another role that doesn’t necessarily require deep technical, scientific understanding of AI algorithms and machine learning. Instead, you could get by with a wide base of knowledge on the AI products and services available within certain sectors, what they do and how they work.
How you can get an AI investing assistant
If you’re convinced that AI is the future, maybe you want to harness it as your own assistant in your day to day life. If you’ve already got 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 usually only reserved for high flying hedge fund clients.
Examples include our Emerging Tech Kit, which uses AI to predict performance across four tech verticals, and then automatically rebalances each week based on those projections.
Or the Forbes Kit, which takes advantage of our relationship with Forbes and uses their proprietary data and natural language processing to find relationships between trending companies, their overall sentiment and their stock prices.
Not only that, but on our Foundation Kits we also offer Portfolio Protection. This uses AI to analyze your portfolio's sensitivity to a range of different risks like interest rate risk and volatility risk, and then automatically puts in place sophisticated hedging strategies to help guard against them.
Like we said, it’s cutting edge stuff, and it’s available to everyone.