Jobs in artificial intelligence are hot right now. And while many schools are adapting their curriculum to include lessons on the subject, it’s still a Shortlist of universities offering Artificial Intelligence major.
Allison Krinsky graduated with a degree in computer science from the University of Washington in 2022. She currently works as a data analyst at JP Morgan, and in her spare time, she creates videos about working in the tech industry. According to her, many fields of study are interchangeable, and in fact, a degree in computer science, mathematics, information science, or data science can lead to a job in the tech industry.
But even though Krinsky went to graduate school to get a job in the tech industry, she says she’s Working in a research lab advanced her career more than anything else. During her year in the lab, she did a lot of other things, including building models and managing databases.
Most AI jobs require technical knowledge, which is checked during the interview process. Candidates should be able to talk about projects they’ve completed, Krinsky says.
“Often during interviews, recruiters asked me about what I had built, what I was doing, and what challenges I had to overcome,” says Krinsky.
While big tech company names may look flashy on a resume, practical experience is crucial to landing a job, says Krinsky. During the internships she completed before working at a research lab, she was given small projects to complete that didn’t require a lot of skills.
—Training is great for confirming that someone hired you because it adds credibility. “But you’re not going to get out of the game if you don’t do the traditional practice,” he explains.
As demand for AI jobs increases, some companies are becoming more selective about what they look for. So if you have limited experience or want to improve your resume, it’s not a good idea to start your own business and improve your skills. There are several paths you can take if you want to work in AI.
One option that Krinsky recommends is: The travel recommendation system is designed using large language models. This project can be implemented with limited technological expertise. The analyst explains this in different ways, for example through rapid engineering, retrieval augmented generation (RAG), or fine-tuning.
Krinsky also proposed creating a system to classify review sentiment using natural language processing. This involves extracting information from text data and sorting it into units such as positive or negative sentiment. Krinsky explains that the model could also be used for financial analysis or to identify investment opportunities or risks.
Advise what project to create
Her other advice is: Create an image recognition or computer vision project. This involves finding a set of labeled images and teaching the computer to recognize what is in them. That’s a good way to learn about neural networks, according to Krinsky.
These projects can take anywhere from one to three months, depending on how much free time you have. Most start with searching the web for data and then require building, training, and tuning the model. Krinsky also recommends creating a report detailing the project process and results to showcase your work.
Designs don’t have to be revolutionary, but you should try multiple datasets and be able to explain what’s happening. Anyone can reproduce the code from the tutorial, so it’s important to add something unique.
“You have to go beyond saying I wrote the code and it somehow works,” Krinsky says.
The above text is a translation from American Edition Insiderprepared entirely by the local editorial office.
Translated by: Dorota Salos
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