Deepita Pai is a Senior Software Engineer at Square in the San Francisco Bay Area, focusing on Identity Verification of customers on Cash App using AI and ML. Her team helps verify over a million customers per month onto Cash App using Identity Verification algorithms. Prior to this, she was a key contributor to making Amazon.com's Customer Reviews GDPR compliant. Her team was responsible for supporting Amazon’s Customer Reviews widget.

Can you tell us about your AI journey? 

My journey in AI began more than a decade ago. The field was still nascent then, so I sought knowledge from online courses and books. This passion only grew after I worked as a Software Engineer at an AI startup in Bangalore, where I worked on an AI-powered, voice-enabled business analytics platform for business users. 

To further my knowledge, I decided to pursue a Master's in Computer Science at the Technical University of Munich (TUM) in Germany, renowned for its accomplished professors and cutting-edge research in AI. During my Master's program, I delved into theoretical AI and its applications across various fields. At this time, I worked as an ML Engineer at Brainlab. 

At Brainlab, I tackled a distinct challenge: applying Deep Learning models to brain signals from EEG (electroencephalogram). I played a critical role in a novel research project that processed these signals to differentiate strokes from concussions - a highly complex problem.

Following my time at Brainlab, I went to Harvard University as a Machine Learning Researcher. At Harvard, I designed a Medical Entity Extraction application researchers use to identify diseases based on patient symptoms. This application was built on Natural Language Processing models to extract symptom-related information from vast amounts of unstructured medical text data, enabling researchers to identify potential disease diagnoses quickly. The application facilitated early detection, resulting in an 8% improvement in early disease identification.

Currently, I'm working on applying AI models within the FinTech industry, ensuring that only trustworthy customers gain access to the product.

Throughout my journey, I have been driven by a passion for leveraging AI to solve complex problems and make a positive impact. I firmly believe that industries such as Healthcare and Fintech are ripe for disruption, and AI holds the key to revolutionising them. We're just getting started!

What is your area of expertise in AI, and what made you choose it?

My area of expertise lies in applying AI and Deep Learning techniques to solve real-world problems, spanning domains such as Medical Imaging, Signal Processing, and Natural Language Processing, and building low-latency, highly available, and reliable systems that can fully utilise the potential of the latest advancements. What truly excites me about this field is the democratising potential of AI across varied industries.

It's inspiring to be a part of work that improves outcomes in critical industries and people's lives in meaningful ways. From early disease prediction to ensuring the safety of critical user-facing systems in FinTech - securely and ethically applying AI in highly regulated industries such as Healthcare and Finance with far-reaching impact is truly exciting. 

Is it easy to be a female leader in AI today? 

I've often found myself in the minority, whether during my time at Brainlab or even at Harvard. I've witnessed firsthand how a lack of diversity can lead to biased AI systems. When the teams developing these technologies do not represent the diverse populations they serve, their biases can slip in unnoticed. This is particularly concerning in healthcare, where AI has the potential to revolutionise patient care but also risks exacerbating existing inequalities if not developed responsibly.

For example, if an AI model is trained on data that underrepresents women in heart disease diagnoses, it may fail to identify heart attacks and strokes in female patients accurately. This is just one instance of how bias in AI can have serious real-world consequences. We need more women at the forefront of AI to ensure these technologies are developed and deployed reasonably.

Describe some challenges you have faced in reaching where you are now.

One of the most significant challenges in my journey is limited access to mentorship and sponsorship. Finding mentors who can provide guidance, support, and opportunities for career advancement is crucial for success in any field.

Early in my career, finding role models who could provide tailored advice for my growth in the industry was demanding. I bridged the gap by attending conferences, proactively networking, and sometimes cold emailing industry leaders and Professors in my field. 

Now that I can give back to this industry, I work actively towards fixing this problem. I advise and mentor technologists and engineers through various platforms.

How do you think startups and corporate companies are moving the needle and supporting more women to participate in tech building/development?

Startups and corporate companies increasingly recognise the importance of diversity and inclusion in the tech industry, and many are taking proactive steps to support and build programs tailored to women engineers' career growth.

Companies are moving the needle by creating targeted programs and initiatives to attract, retain, and advance women in tech roles. For example, some companies offer mentorship and sponsorship programs that pair female employees with senior leaders who can provide guidance, support, and opportunities for career growth.

Another way that companies are supporting women in tech is by investing in education and offering grants to conferences such as Grace Hopper's Conference. These events provide valuable opportunities for women to enhance their skills, gain knowledge, and network with other professionals in the industry. By supporting women's participation in these conferences, companies demonstrate their commitment to their growth and success.

In addition to these initiatives, many companies offer perks to support women's unique needs and experiences in tech. These can include flexible work arrangements, parental leave policies, and employee resource groups (ERGs) that provide a sense of community and support for underrepresented groups. ERGs can also be vital in driving new initiatives and programs that promote gender diversity and inclusion.

What do you want to say to women who wish to build careers in AI and other tech-related fields? 

My key advice is to not shy away from taking bold risks, stepping outside your comfort zone, and never having imposter syndrome. Apply for that challenging project, take on a leadership role, or start your own company. Embracing risks and learning from failures are integral parts of professional growth. 

If you see a gap in the industry and want to build a tool to fix it, consider starting your venture. Surround yourself with a supportive network, seek out mentors, and don't be afraid to ask for help when needed.

As you navigate your career in technology and AI, remember that taking risks benefits your personal growth and the advancement of the industry. By bringing your unique perspectives, ideas, and leadership to the table, you contribute to creating a more diverse technological landscape.