With over 20 certifications in Data Science and AI from reputed companies and institutions, Yaasna Dua is one of the top upcoming women data scientists currently working at McKinsey & Company. Yaasna is known for her quick designing skills while conducting data science experiments with strong mathematical fundamentals and programming skills. 

Can you tell us about your AI journey?  

After graduating from Delhi College of Engineering (DCE), I started my career as a Java developer. Seeing the impact of AI/ML on the breadth of industries like Healthcare and Finance to Human Resources attracted me to pursue a career here. I started with Andrew NG's courses on Machine Learning, where I saw the potential of AI/ML to create an impact on a scale by delivering reliable solutions to problems impacting businesses and people.   

Data science is a multi-faceted skill set. I started my upskilling journey by reading articles, participating in hackathons, getting recognized data science certifications, and listening to talks from global AI leadership. I believe my experience in hackathons and my credentials helped me break into my first Data Science role at Publicis Sapient. That kick-started my AI journey and paved my way to work as a data scientist in Naukri.com, where I handled the full life cycle of a data science project, from gathering and cleaning data to building models and deploying ML systems. I then joined my current firm McKinsey & Company, where I have closely worked with clients to solve their employee lifecycle problems using data-driven decision-making.  

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

My key motivator has been a real-life large-scale impact. I developed expertise in Recommendation Engines and NLP while working at Naukri.com, where I built job and course recommendation engines, helping 6 million + people per week in their job search. At McKinsey, I continued making scalable Data Science Products. Apart from managing the life cycle of ML projects, my area of expertise expanded to stakeholder management and data-driven decision-making. After working as a data scientist for eight years, I have realized that before building ML products, it is important to facilitate a change in leadership mindset from intuition-based to data-driven decision making.   

Can you say something about your current role and your handling projects?     

In my current role at McKinsey, I manage projects which help our clients make data-driven decisions about the Employee Life Cycle. I also work on creating data science products which facilitate long-term Strategic Workforce planning, gathering Talent Market Intelligence and benchmarking clients' talent against their competitors at scale. These products, in 4+ years, have managed to touch 400+ companies across multiple geographies.  

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

I have been lucky that my family and all my employers have been supportive and encouraged me to pursue opportunities that have facilitated my growth as an individual and as an AI professional. However, one of the biggest challenges I faced was a lack of formal support network, sponsorship and coaching. For upskilling, I took MOOCs and leveraged LinkedIn to connect and follow industry thought leaders.  

Do you see enough female leadership roles in corporates? In your opinion, what should change?  

I was reading recent research by McKinsey, which stated that women's representation in the workplace at senior leadership positions is almost 1/3rd of that of men. The baseline in the funnel keeps decreasing. As for 100 men promoted, only 86 women are encouraged. I think the first step should be to increase awareness about this trend and incorporate diversity, equity, and inclusion (DEI) best practices as part of the policy to consciously remove bias in hiring and promotion. Research suggests this will lead to a diversified talent pool, bringing in new perspectives leading to innovation and improved performance. Establishing formal avenues of mentorship, networking and allyship networks can also create a strong pipeline of women professionals and women leadership in the industry.  

Discrimination by AI is a genuine concern while ensuring data integrity - does this exist? If so, how can it be addressed?     

The outcome of any intelligence system depends on the quality of inputs fed into it. Therefore, monitoring the data sources to eliminate intrinsic structural biases is critical. Furthermore, with systems moving forward in their automation lifecycles, it is key to have humans in the loop to investigate, address and correct any bias against any community, especially minorities.  

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

AI is a dynamic field which is seeing exponential innovation and growth. Because of the nature of this field, it is important to keep upskilling yourself and keep abreast of the latest so that you are ready to leverage interesting and potentially life-changing opportunities. Women should actively seek to develop support networks for coaching, mentorship and sponsorship and not hesitate to ask for help. You will face failures but must persist and give up the fear of failure. This mindset has helped me take calculated risks and grow both as a person and an AI professional 

Disclaimer: The views expressed in this article are of Yaasna Dua and not of her employer.