Data Science Institute champions women in data science and tech roles


IWD event hosted at the Data Science institute

Imperial’s Data Science Institute hosted a panel discussion for International Women’s Day, celebrating female careers in data science, AI and tech.

Friday 8 March marked International Women’s Day and the conclusion of Women at Imperial week, this year themed around ‘investing in women and accelerating progress.’

According to the Alan Turing Institute, women make up just 22% of AI and data professionals and 18% of users across the largest online global data science platforms.

In light of this, the Data Science Institute (DSI) hosted an insightful panel discussion highlighting the triumphs and challenges faced by women in these predominantly male-dominated sectors.  

The panel featured Shuojie Fu, a Research Assistant in Data Curation and Data Science at the Department of Surgery & Cancer and Imperial’s Data Science Institute; Hana Makhlouf, a Software Engineer at Bank of America; and Dr Giulia Ferrari, a Research Fellow at our sister Data Science Institute at The London School of Economics and Political Science. Their varied career backgrounds helped to highlight important stories and advice from roles across academia and industry.

Reflecting on her experiences Shouojie said: “Sharing stories with women from different domains and life stages is such an inspiring experience. I feel proud of how strong women are in our ways, when facing challenges from both work and life. ”

Bridging the digital gender divide

The fields of AI and data science have grown exponentially as the world is increasingly being built around automated systems and intelligence machines. However, those driving these advancements often fail to mirror the diversity of the societies they aim to serve.

As AI pervades daily life, the imperative for diversity and inclusivity within technology has become paramount. In particular, the persistent under-representation of women and marginalised groups in data science and AI, including gender data gaps, leads to the encoding and amplification of bias in technical products and algorithmic systems, creating harmful feedback loops.

Encouraging more women to participate in these fields and learning from each other about how we can make more inclusive workplaces is therefore a crucial activity to participate in.

Those that attended the session found it highly engaging, describing it as "interactive and useful".

One participant explained: "It was wonderful to hear the experiences and advice shared during the panel discussion. As a woman working in the field of data science myself, I found the discussion to be incredibly inspiring and valuable."


Gemma Ralton

Gemma Ralton
Faculty of Engineering

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