Gen Z Data Scientist Debunks Myths About AI Jobs

Working in the AI industry is more than just writing code.

Just ask Pranjali Ajay Parse, 25, a data scientist at Autodesk who is developing an AI tool that gives employees insight into their work patterns, such as meeting trends and work routines.

After earning a master’s degree in computer science and working at Autodesk for more than a year, Parse came to understand what it’s really like to work in an AI role, which she admits isn’t what people might expect.

Parse said that AI jobs are largely interdisciplinary and collaborative; and while you may work in technology, the job also requires a strong focus on ethics. In an interview with Business Insider, she debunked some myths about AI roles.

It’s not just coding

Pranjali said that proficiency in Python is not enough if you are looking for a job in AI.

Parse said candidates don’t necessarily need a degree in AI to get a job in the field. However, she said you do need to be able to analyze a case study, run SQL queries and code. She said candidates can try boot camps or personal projects to develop skills in those areas.

“AI is inherently interdisciplinary,” Parse said. “It draws conclusions from a variety of fields, including mathematics, computer science, statistics, and domain-specific knowledge.”

Parse said that about 70% of her work is data science, which involves reviewing and analyzing datasets. She said the rest of her time is split between software engineering, building pipelines, data engineering, architectural design and a lot of math.

Parse also added that it is important to stay abreast of advances in related fields as the technology is constantly evolving.

AI roles often require close collaboration

Software engineers are known to be loners, but don’t count on loneliness if you work in AI.

While some engineering roles tend to be independent, Parse said that “AI projects are rarely solo projects.” That’s partly because AI is a new technology that requires collaboration across teams and stakeholders, she said.

For example, Parse said it had to work with seven or eight teams to build an AI-powered recommendation system project.

In her experience, the process starts with the data science team collecting and preparing data. Then, data scientists apply statistical methods and modeling. Then, the machine learning team develops and refines the model. Once the model is ready, UX and UI experts design the user interface, and then software engineers build the front end.

Finally, the marketing team established a product launch strategy.

“A comprehensive AI project requires a lot of communication and collaboration,” Parse said.

You have to think about ethics

Privacy teams are often heavily involved in handling sensitive data during AI development.

Parse said privacy protocols are extensive. When working with human data, workers must obtain permission to perform tasks. The projects also require robust production measures, such as pseudonymizing identities and ensuring that models “don’t inadvertently reproduce biases or create unfair outcomes.”

That requires adhering to legal and regulatory requirements, she said. It also means thinking about the long-term implications of projects, including potential unintended consequences and ethical dilemmas.

While privacy may seem like an obvious issue for those working in AI, Parse said it’s easy to get caught up in how the models are working. Plus, because so many teams contribute to the product, it’s easy to focus on a specific task rather than the overall implications, she added.

Parse said it’s up to companies to train employees on proper privacy and ethics. But it’s also important for employees to consider third-person perspectives on the work they do.