AI-related careers are popular right now. Even though a lot of universities are changing their curricula to include courses on the subject, the number of universities that offer an AI major is still quite small.
In 2022, Allison Krinsky received her informatics degree from the University of Washington. She currently works at JPMorgan as a data scientist and spends her leisure time creating movies about tech-related careers. According to her statement, a number of majors can be combined to create a variety of career paths. These include computer science, math, information sciences, and data science.
Despite completing a conventional education to obtain a tech job, Krinsky claimed that her employment in a research lab was what really helped her career progress. She added that among the “heap” of things she accomplished in her year at the lab were managing databases and creating models.
According to Krinsky, candidates must be able to discuss their projects throughout the interview process, as most AI-related positions involve a technical component. During many of her interviews, the questions that would come up were centered around what she had built, what she did, and the difficulties she had encountered. According to her, having practical experience is essential to getting hired, even though Big Tech companies may appear impressive on your resume. She claimed she was assigned simple projects that didn’t require a lot of abilities in her previous internships before joining the research lab.
According to Krinsky, the internship is fantastic since it gives her legitimacy to state that someone hired her. If you haven’t had a traditional internship, though, you’re still in the running.
Some organizations are becoming more selective in their hiring as the demand for AI positions rises. Therefore, it’s not a bad idea to construct your own project and skill up if you have minimal experience or if you want to improve your resume. According to Krinsky, there are several paths you could take based on the kinds of jobs you’re drawn to.
A trip recommendation system constructed with large language models is one solution that Krinsky suggests. She explained that this project might be completed by someone with little knowledge and in a variety of ways, such as through retrieval augmented generation, prompt engineering, or fine-tuning.
Krinsky also proposed developing a sentiment categorization system for reviews using natural language processing. She explained that this includes extracting information from text data and categorizing it into categories such as positive or negative thoughts. Krinsky stated that this can be utilized for financial analysis or to detect investment opportunities or risks.
Krinsky suggested that you try an image recognition or computer vision project. This entails locating a series of pictures with labels and teaching a computer to recognize what is in the images. She described it as a good method to learn about neural networks.
According to Krinsky, these projects can take anywhere from one to three months, depending on how much time you have available. Most initiatives begin with data scraping from the web, followed by model development, training, and fine-tuning. Krinsky also suggested writing a report outlining the project process and outcomes so you have something to show for your efforts.
She stated that the initiatives do not need to be groundbreaking, but they should explore with multiple data sets and be able to explain what is happening. She stated that anyone may duplicate code from a tutorial, so it’s critical to provide a unique component.
“You must get past ‘”I just wrote code and it didn’t break,” Krinsky stated.
