Artificial Intelligence (AI) is a fast-growing tech field that has helped companies of all sizes improve the efficiency and effectiveness of their services and products. But don’t you have to be a PhD to work in AI? Wrong! Carianne Burnley, a Career Coach at Springboard, breaks down the career path from beginner to director and tells us about 5 job titles you might apply for in artificial intelligence. Plus, find out how Carianne supports students in landing their dream AI careers after Springboard’s bootcamps.
Humans take in a large amount of information and make decisions based on that information. The field of Artificial Intelligence teaches machines to take in a large amount of information and think and make decisions like humans.
Artificial Intelligence is an umbrella term, while machine learning is a subset of artificial intelligence. AI is a vast field, so if you are interested in a career in AI, then machine learning is typically a great place to start.
Everyone is either using or being used for AI purposes. One of the most common examples of artificial intelligence today is Netflix, which uses the data points based on watching history to fuel their recommendation services. Another example is in wildlife management, where AI is being used to track animal movements to decide how animals need to be protected to ensure their continued survival. And, a fun student example is in crop management. This student was able to predict the yield of a crop field based on computer images. Regardless of your interest or passions, you will likely find a way to use or be used by AI or machine learning
A Machine Learning Engineer focuses on research, design, and implementation of models to help machines make choices. Their goal is to create software that works with minimal human interaction to gain insights. For example, a Machine Learning Engineer may give a computer features to analyze for classification of dog and cat images. The more images the computer obtains, the more it will show if the classification is working or not, and the Machine Learning Engineer may change the features to keep making it more and more accurate.
A Data Scientist creates predictive models to solve real-world problems. They focus on obtaining insights from data and presenting them. A good example of what a Data Scientist may do happened in 2020, a model was created around“flattening the curve” for the COVID-19 pandemic.
A Data Engineer takes data from multiple sources. This data is typically a huge mess. Data Engineers cleanse the data, massage the date, pipeline the data, etc. ensuring it is formatted correctly for the intended usage.
Software Engineers create a software solution using knowledge of programming languages, and sometimes the role is focused specifically on data and AI. Students who land here oftentimes land in software engineering jobs that heavily utilize Python.
A Product Manager identifies what users find valuable in a product. They have to have a solid understanding of the product, so if the product is using AI/machine learning techniques - they would have to understand these concepts.
What is the typical career path in AI?
It depends on your initial background. For a beginner, a non-technical person, an entry-level Data Analyst or Business Analyst may be your starting point. This role can lead you to the artificial intelligence or machine learning career path as you may be working closely with these teams and grow a desire to go deeper into the technologies associated with this field.
For a technical person, somebody with a background in math, statistics, programming,there is potential that you may start in a more hands-on technical role like a Data Scientist or Machine Learning Engineer role. A career path can look something like:
What is the Career Outlook for AI jobs in 2021?
Artificial Intelligence is a growing field! The World Economic Forum expects that between 2020-2022, AI jobs will jump from 78 to 123 of every 10,000 jobs.
From a student’s perspective, there are many jobs out there. In a recent search of Machine Learning careers with a Springboard student, 72,000 jobs came up. And, if you do a similar search for AI jobs, 124,000 jobs appear in the search.
Do you typically see more careers in Artificial Intelligence than Machine Learning?
Yes, the raw numbers do show that there are more AI jobs than Machine Learning jobs. However, it depends on who is writing the job description and their understanding of the differences between the two areas. So many words in job descriptions are used interchangeably. You can look at an AI position and see that the job responsibilities align more with the day-to-day responsibilities of a Machine Learning Engineer or a Data Scientist. This is where Career Coaches can really help a student. It’s great to search by Machine Learning Engineer job titles, but oftentimes much better to search by Python and Engineer to see more roles in this area and have less competition!
Tell us some exciting hiring partners who hire from Springboard!
Some of the big name hiring partners include: Microsoft, Amazon, Home Depot, and Booz Allen. We have many mid-size, and start-up companies hire alumni of our programs as well. I think it is fair to say that Springboard is doing a great job preparing students for new and exciting career paths.
Anybody! But, a successful AI Engineer will have enough technical skills needed for the day-to-day job responsibilities and will also have the soft skills necessary to contribute to a healthy team and culture. Soft skills are taught at Springboard primarily through career coaching. If you are interested in becoming an AI Engineer, you will likely have these three soft skills:
Is AI a field for anyone?
Absolutely. But, AI can be a marathon goal if you aren't from a technical background. A “couch to 5K” mentality can be a more realistic goal. So, if you don't have a technical background but want to dip your toes into the field, it'd probably be best to think about a Data Analytics bootcamp. Once this goal is completed, find a Business Analyst or Data Analyst role and be super curious and willing to learn. This may lead you into getting a more hands-on type of role on the in depth hands-on technology side of things.
Do we need more women and people of color in AI?
Yes, most technical fields need more women and people of color! A recent study from the AI Now Institute predicted that a diversity disaster is coming up in AI systems! If you’re not accounting for different faces, colors, or features in your modeling, you’re going to miss out on users or, worse, train computers to do bad things! We need people to account for these differences in models and all types of people working within the field can help ensure that this disaster doesn’t happen.
But, diversity in tech depends on employers hiring what differing types of people offer. If you are ONLY willing to hire people with certain degrees or years of experience for certain roles, you’ll only get people who have routes to these very specific degrees and years of experience. From my perspective, we need more value placed on players, not positions. We need employers to see the value in people who haven’t had access to college degrees, or people who take time off to do life (whatever life is for their situation), or somebody who decides at sixty to make a career change, etc.
What are your favorite resources for beginners who want to get into artificial intelligence?
A fun start to this field would be reviewing the history of artificial intelligence here.
Then, watch this video about the Machine Learning Lifecycle.
Then, maybe watch some videos on YouTube of Andrew Ng – he was the Co-Founder and Head of Google Brain.
My favorite is reviewing this field from the job description standpoint. Do a search on Machine Learning or AI Engineer. Or, if an Engineer title sounds too daunting, search on Analyst and “machine learning” or Analyst and AI. Read through the responsibilities and requirements and you’ll begin to see what the market looks for in these types of roles.
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