Q: Can you talk about your progression at Arm in various roles, and what that shows about women’s growth in data and AI?
I joined Arm to build our engineering capacity planning function, forecasting resources for key platforms like HPC, Storage, Emulation, FPGAs and cloud. That work led to building an interdisciplinary team across application engineering, data science and data engineering.
We created predictive models that saved millions. This became our Operations Research and Decision Analytics function, applying data and programming to optimize enterprise-scale investment decisions.
Today, I lead Data Strategy, guiding how we embed AI and data in decision-making across Arm.
My path hasn’t been linear. I’ve seen women move between technical depth and leadership. I’ve done the same. I've balanced empathetic leadership with vision, enthusiasm, and determination to earn trust and empower teams. I did these detours building on my technical experience.
Staying curious, learning continuously, and delivering value leads to growth. Whether as a technical specialist or a strategic lead, there’s space to move, contribute and lead.
Q: How about mid-career professionals who might be pivoting into data and AI?
Your domain experience is a strength. Businesses need people who understand both context and analytics: supply chains, finance, user experience.
Upskill in areas like cloud architecture, SQL or model deployment, then show how Data and AI solve problems in areas you already know well.
Be ready to lead. Often, you’ll be the bridge between technical experts and execs.
Q: How can companies attract diverse talent and support inclusive career paths?
Show up intentionally, where you can make an impact. In India, we visit women-only colleges. In the US, we partner with historically black institutions to attract talent.
We also open new pathways; from the Arm Developer Program to UK apprenticeships. Where traditional routes fall short, these help people get in and grow. Inclusion starts with intention.
Inclusion isn’t just about inviting you to the table. It’s handing you the mic, backing your ideas, and cheering while you run with them.
Q: How does bias in AI lead to new kinds of roles?
Bias in AI is technical, organisational and human. That’s why we’re seeing roles like model fairness testers, AI engineers, prompt engineers and customer-facing AI/BI analysts.
Arm’s AI Office brings together engineers, legal, architecture and policy teams. That kind of collaboration is now critical.
When we build Data and AI products — dashboards, models, analysis — we bring in diverse voices early on. That helps us spot blind spots and build tech that works fairly for everyone. Products that customers can trust and use.
Q: How does culture and leadership help drive inclusion?
Inclusion starts with leaders. At Arm, ‘building extraordinary teams’ means recognizing strengths, inviting new perspectives and staying open to challenge.
Every Arm product, especially in AI and data, is built by cross-functional teams. Diverse teams deliver better outcomes. That’s why we create them with intent.
Inclusive leadership means people feel safe to speak up. When voices go unheard, innovation stalls.
Inclusion isn’t a slogan; it’s culture in action. At Arm, we challenge skilfully. We value difference. That’s how we build world-leading products — through bold collaboration.