Home AI News Closing the Gender Gap in STEM: Insights from Women in Machine Learning

Closing the Gender Gap in STEM: Insights from Women in Machine Learning

Closing the Gender Gap in STEM: Insights from Women in Machine Learning

The Gender Gap in STEM: A Collective Effort

The gender gap is a well-known issue in the STEM field. Despite some progress, women still only make up a quarter of the STEM workforce in the UK. Many women feel held back due to a lack of representation, opportunities, and information about working in the industry. Closing this gap requires a collective effort from everyone in the field.

Organizations like Women in Machine Learning (WiML) are actively working to create a more inclusive environment and amplify the successes of women in STEM. They serve as a valuable resource for women who want to learn more about working in the field. In honor of International Women in Engineering Day, we reached out to the WiML community to discuss the most common questions they receive about technical interviewing.

To provide insights and discuss what it’s like to work at DeepMind, we spoke with Mihaela Rosca (Research Engineer), Feryal Behbahani (Research Scientist), and Kate Parkyn (Recruitment Lead – Research & Engineering).

Am I ready to apply for a role in the industry?
Mihaela: It’s common to have doubts and feel underprepared when applying for positions in the field. There’s never a perfect time to apply, and you might always feel like there’s more to learn. But don’t let that discourage you. If you’re interested in working on the future of machine learning research and already have knowledge of research papers and implementing algorithms, you’re ready to apply. Interested? Learn more about our research and engineering teams.

What metrics are important for hiring?
Kate: We have different hiring criteria depending on the role. For research scientists, we don’t focus too much on publications or academic achievements. We’re interested in past internships, voluntary industry experiences, and proven abilities in research, engineering, and application. For research engineers, we look for people who enjoy applying theory in a computational form. Software engineers should have clear problem-solving and communication skills. Showing evidence of similar projects or experiences in accelerating research is also valuable.

Any tips for writing a successful CV?
Kate: Creating a good CV is essential. Keep it concise, around two pages. Include additional information like programming languages, societies, awards, and volunteering experience. Be consistent with font and formatting, and don’t forget to proofread for grammar and spelling errors. Include relevant technical skills and links to your personal Github, LinkedIn, or portfolio.

Any recommended resources for professional development?
Feryal: There are various resources available for learning and developing machine learning skills. YouTube offers open-access introductory courses on Deep Learning and Reinforcement Learning by Nando de Freitas and David Silver respectively. Blogs like Distill provide overviews of specific techniques, and conferences like NeurIPS, ICML, and ICLR publish advanced machine learning research. Summer schools like MLSS and DLRLSS also provide learning opportunities. Organizations like WiML are excellent for building technical confidence and amplifying achievements.

What can I expect in the interview process?
Feryal: The interview process at DeepMind varies depending on the role. For a Research Scientist role, it consists of four phases. The first phase is an initial chat with the recruitment team to discuss your background, experiences, and motivation. The second phase includes technical interviews and coding exercises. The third phase involves research interviews to discuss your specific research background and interests. The final phase is a culture interview to align your career goals with DeepMind’s mission.

How important are research skills versus coding ability in technical interviews at DeepMind? How did you prepare for your technical interview?
Mihaela: Technical interviews at DeepMind assess both research skills and coding ability. The first stages focus on theoretical knowledge, while later stages test coding skills. The key is to demonstrate problem-solving skills and effective communication. When preparing for my own interview, I reviewed my university lecture notes and researched topics I was less familiar with, like reinforcement learning. I also practiced coding questions in Python using a simple text editor to improve my speed.

Can research engineers lead research projects?
Mihaela: Definitely! Research Engineers at DeepMind and elsewhere often lead projects of all sizes. They can lead research projects and provide valuable contributions to the field.

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