No one ever said a career in data science was easy, especially for women. It requires years of study, a passion for advanced mathematics, an ability to translate large volumes of behavioural data into findings that help companies and organizations make better decisions, all while navigating these factors in a mostly male profession.
Yi He is a principal data scientist for the Innovation Labs at Experian. She spoke with the Digital Journal about her role as a visionary and leader in data science, using new technologies such as machine learning and generative AI to help businesses engage with consumers in more meaningful ways.
Digital Journal: Is there something specific that is drawing more women to the profession of Data science?
Yi He: Elementary, middle and high schools have placed more emphasis on STEM and careers in these fields, which is a great. Programs like Girls Who Code, along with similar organizations, help to get more people, including women, interested in finding patterns in data or in behaviours to solve complicated problems.
My mentor Marcia K. Johnson at Yale University was one of the industry’s great pioneers. Her ground-breaking research in brain imaging and bias detection plays an important role in how I approach my work on a daily basis.
It is very important to me to pay it forward, become a role model like Marcia and inspire a new generation of data scientists.
DJ: How is the data science role becoming more innovative?
Yi He: Traditionally, data science has been focused more on mathematics and problem-solving, and coding skills to manage big data and extract valuable insights to help drive more meaningful customer decisions. More recently, data processing and analysis involve gaining a deeper understanding of what drives individual or corporate decision-making, which lends a distinct psychological element to the work we do.
Data-driven analytics and modelling on behaviours helps brands with real-time decisioning and offering deeper personalization of service delivery from web-based applications. It also helps ascertain whether – during an online transaction – that brand is engaging with a real consumer, or a synthetic or “fake” identity built by a fraudster. This issue has become quite serious with new technologies, such as OpenAI’s ChatGPT and other generative AI solutions, that can scrape the internet and dark web for identity data then create synthetic identities based on that information in near real time.
DJ: How does data science help to protect businesses and consumers?
Yi He: As the principal scientist in Experian’s Innovation Labs, I work on technology solutions that help fight AI fraud with AI. These integrated technology solutions aggregate data from a company’s disparate identity verification, fraud prevention, authentication, and security and cyber controls across the enterprise. We evaluate all the risk signals, including authenticated identity data, device risking, email and mobile risking capabilities, behavioural biometrics, document verification, and more.
By analysing patterns in data and identifying potential risk factors, my team and I help companies instantly spot early indicators of potentially fraudulent behaviours.
DJ: You mention that technology can help protect consumer data and identity. Can you explain a little more about your work in this area?
Yi He: All of us interact with websites on a daily basis that confirm that we are who we say we are with a service called identity verification and resolution. This includes transferring money to and from financial accounts, interacting with a healthcare provider, buying groceries on Amazon or binging a series on Hulu.
That’s why you are sometimes asked to input the answer to a question like your first pet’s name or input a code sent to your mobile phone. Websites process and confirm our identities in a matter of seconds.
DJ: How do you apply data science in practice?
Yi He: My team and I use data science to help organizations that run large websites manage millions and millions of consumer identities safely and securely despite the increasing instances of fraud. For example, we have developed an integrated, multi-layered fraud and identity platform that empowers our clients to uncover data-driven business opportunities while delivering fast, personalized and seamless customer experiences — without sacrificing privacy or compromising fraud detection.
It easily permits additions of point solutions, then consolidates and orchestrates both identity verification and fraud risk signals into a single view for more efficient decision making and isolating data-driven business opportunities through segmenting, targeting, acquiring, and engaging consumers.
DJ: What is one area of the data science field that is truly exciting?
Yi He: Generative AI will impact all our lives, especially when it comes to text, audio and video content development. While the technology enables companies and people to experience increased productivity and efficiency, it raises ethical and business issues.
For instance, you might have heard that authors such as Sarah Silverman, Mona Awad and Paul Tremblay have sued OpenAI for copyright infringement. This is because ChatGPT has access to a data set that features copyrighted text that users could leverage from other written works. This brings up important questions such as is generative AI gathering information ethically? Or can AI-generated content be copyrighted?
This is the same argument that athlete Olympic gold medalist Allyson Felix made to Nike about the use of her name, image and likeness in perpetuity when she recently tried to renegotiate her contract. This was also one of the main components of the Hollywood actors and writers strike, where the new writer’s contract stipulates that Generative AI is a tool, not a replacement for their work.
These are all important issues that we’ll continue to thoughtfully discuss as AI technology advances, and we will need to be prepared for whatever societal evolutions arise in how machine learning gathers and uses data.
DJ: What advice do you have for other women who are in the data science field?
Yi He: Believe in your achievements – they are just as significant as the accomplishments of others on your team. This mindset boosts confidence and expression within a team environment.
Always push your limits. With enough motivation and effort, you can be anything that you want to be.
Lastly, don’t let your gender or background stop you from achieving your dreams. Growing up in China, I have seen males receive more educational opportunities compared to their female counterparts. Girls and women are deserving of these opportunities, too.
This is one of the reasons why I am passionate about my role with Experian. Some of my work involves using data to help people in underserved communities in the U.S. increase their credit scores to do things like qualify for a car loan or rent an apartment and reach important life milestones to achieve financial prosperity.
