Yaasna Dua, Data Scientist, McKinsey & Company


With more than 20 data science and artificial intelligence certifications from reputable companies and institutions, Yaasna Dua is one of the top upcoming data scientists currently working at McKinsey & Company. Yaasna is known for her rapid design skills while conducting data science experiments with strong mathematical foundations and programming skills.

Can you tell us about your background in AI?

After graduating from Delhi College of Engineering (DCE), I started my career as a Java developer. Seeing the impact of AI/ML spanning industries such as healthcare and finance to human resources inspired 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 impact at scale by providing reliable solutions to problems affecting businesses and people. .

Data science is a multi-faceted skill set. I started my developmental journey by reading articles, participating in hackathons, earning recognized data science certifications, and listening to talks from global AI leaders. I believe that my experience in hackathons and my qualifications helped me break into my first role in Data Science at Publicis Sapient. This kickstarted my AI journey and paved the way for me to work as a data scientist at Naukri.com, where I managed the full lifecycle of a data science project, from collection and from cleaning data to building models and deploying ML systems. I then joined my current firm McKinsey & Company, where I worked closely 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 main motivation was real impact on a large scale. I developed expertise in recommendation engines and NLP while working at Naukri.com, where I built job and course recommendation engines, helping over 6 million people per week in their job search. ‘use. At McKinsey, I continued to build scalable data science products. Besides ML project lifecycle management, my area of ​​expertise extended to stakeholder management and data-driven decision making. After working as a data scientist for eight years, I realized that before building ML products, it’s important to facilitate a shift in leadership mindset from intuition-based decision-making to a data-based decision making.

Can you tell us about your current role and your management plans?

In my current role at McKinsey, I manage projects that help our clients make data-driven decisions about the employee lifecycle. I also work on building data science products that facilitate long-term strategic workforce planning, talent market intelligence gathering, and benchmarking of client talent against their large-scale competitors. These products, in more than 4 years, have managed to reach more than 400 companies in several geographical areas.

Describe some challenges you faced in getting to where you are now.

I have been fortunate that my family and all of my employers have supported me 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 the lack of a formal support network, mentorship and coaching. To improve my skills, I took MOOCs and used LinkedIn to connect and follow industry thought leaders.

Do you see enough female leadership positions in companies? In your opinion, what should change?

I was reading a recent study by McKinsey, which indicated that the representation of women in the workplace in leadership positions is almost a third of that of men. The baseline in the funnel keeps decreasing. Out of 100 men promoted, only 86 women are promoted. I think the first step should be to raise awareness of this trend and incorporate Diversity, Equity and Inclusion (DEI) best practices as part of the policy to consciously eliminate bias in hiring. and promoting. Research suggests this will lead to a diverse talent pool, bringing new perspectives leading to innovation and improved performance. Establishing formal pathways for mentorship, networking, and networks of allies can also create a strong pipeline of female professionals and female leaders in the industry.

AI discrimination is a real concern while ensuring data integrity – does it exist? If so, how to fix it?

The outcome of any intelligence system depends on the quality of the inputs fed into it. Therefore, monitoring data sources to eliminate inherent structural biases is essential. Additionally, with systems advancing through their automation lifecycles, it is essential to have humans in the loop to investigate, address, and correct any bias against any community, especially minorities.

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

AI is a dynamic field that is experiencing exponential innovation and growth. Due to the nature of this field, it is important to continue to improve your skills and keep up to date with the latest developments so that you are ready to take advantage of interesting and potentially life-changing opportunities. Women should actively seek to develop support networks for coaching, mentoring and sponsorship and not hesitate to ask for help. You will face failures, but you will have to persevere and let go of the fear of failure. This mindset has helped me take calculated risks and grow both as a person and as an AI professional.


About Author

Comments are closed.