Data scientist Pranjali Ajay Parse explains what a job in AI looks like in reality – and what interdisciplinary skills and ethics have to do with it.
There’s more to a job in artificial intelligence than just writing code: just ask 25-year-old Pranjali Ajay Parse, a data scientist at Autodesk. She develops an AI tool that gives employees insights into their work behavior, such as meeting trends and work routines. After completing her master’s degree in computer science and working at Autodesk for over a year, Parse was able to get a sense of what it’s like to work in an AI job.
And she says it’s not what people might expect. Parse explains that work in AI is largely interdisciplinary and relies on collaboration; and while you might be working technically, the job also requires a strong focus on ethics. In a conversation with Business Insider, she dispelled some of the myths about AI roles.
It’s not just about programming
Parse says Python skills are not enough to get a job in artificial intelligence. Applicants don’t necessarily need a degree in AI to get a job in the field, but they do need to know how to analyze case studies, run SQL queries, and program. She says applicants can try bootcamps or personal projects to gain skills in these areas. “AI is interdisciplinary by nature,” Parse says. “It draws on various fields, including mathematics, computer science, statistics, and domain-specific knowledge.”
Seventy percent of her work is data science, which requires reviewing and analyzing data sets. The rest of her time is split between software engineering, building pipelines, data engineering, architecture design, and a lot of math. Parse adds that it’s important to stay up to date with advances in related fields because technology is constantly evolving.
AI activities are often very collaborative
Software engineers are known for being loners, but don’t expect loneliness when working in AI. While some engineering roles are more independent, “AI projects are rarely done alone,” says Parse. This is partly because AI is a new technology that requires collaboration between a variety of teams and stakeholders. The data scientist says that she has to interact with seven or eight teams to develop an AI recommendation system project, for example.
In her experience, the process begins with data collection and preparation by a data analytics team. Then, data scientists apply statistical methods and modeling. The machine learning team then develops and refines the model. Once the model is ready, UX and UI experts design the user interface, followed by software engineers who develop the frontend. Finally, the marketing team sets the strategy for bringing the product to market. “A comprehensive AI project requires a lot of communication and collaboration,” says Parse.
You have to think about ethics
Privacy teams are often deeply involved in the process when AI development involves handling sensitive data. Parse says privacy protocols are comprehensive. When employees work with a person’s data, they must be given permission for their tasks. Projects also require robust production measures, such as pseudonymizing identities and ensuring models “don’t inadvertently create bias or produce unfair outcomes,” which requires compliance with legal and regulatory requirements, she says. It also means thinking about the long-term impact of projects – including potential unintended consequences and ethical dilemmas.
While privacy may seem like an obvious consideration for those working in AI, Parse says it’s easy to get lost in the performance of the models. And with so many teams working on the product, it’s easy to get focused on your specific task rather than the overarching impact, she adds. Parse says it’s up to companies to train employees on proper privacy and ethical guidelines. But it’s also important for employees to take a third-party perspective on their work.
Source: https://www.businessinsider.de/gruenderszene/technologie/data-scientist-verraet-diese-faehigkeiten-braucht-ihr-wenn-ihr-in-der-ki-branche-arbeiten-wollt/
