Yogananda Domlur Seetharama (Yog) is the Director of AI Labs at Sam’s Club, part of Walmart Global Tech, where he spearheads advancements in Search, Personalization, Ads, and Anti-Fraud using cutting-edge machine learning algorithms. With over a decade of data science leadership, Yog has significantly enhanced Sam’s Club’s operations, contributing to its $86 billion revenue. An Oklahoma State University alumnus and advisory board member, he holds multiple patents in AI and actively contributes to the machine learning community. Yog’s expertise in integrating AI technologies has set new benchmarks for retail innovation and efficiency.
Recently, in an exclusive interview with Digital First Magazine, Yog shared his professional trajectory, insights on the future of the data science market in the next five years, the best piece of advice he has ever received, significant career milestones, future plans, words of wisdom, and much more. The following excerpts are taken from the interview.
Yog, at what point in your life did you realize that you wanted to get into data science/data analytics and why? Was there a specific “aha” moment when you realized the power of data?
During the early 2010s, I noticed a pattern of rapid digitization and exponential growth in Big Data across various industries. This observation piqued my curiosity and led me to pursue a master’s degree with a focus on statistics. The credit for my deep interest in data analytics goes to my then Professor of Marketing Analytics, Dr. Gautam Chakraborty, whose lectures and insights vividly illustrated the potential and future of data analytics. His passion for the subject and ability to connect theoretical concepts with real-world applications opened my eyes to the transformative power of data in making informed decisions and uncovering hidden patterns.
An impactful part of my journey was the internships and research projects I undertook, particularly in social media analytics. These experiences gave me a hands-on understanding of how the right tech skills and tools could be leveraged to derive meaningful insights from vast amounts of data. Furthermore, working on real-world data problems during these internships allowed me to appreciate the nuances of data quality, data cleaning, and the importance of context in data interpretation. These experiences reinforced my belief in the power of data and solidified my decision to build a career in data science and analytics. The journey has been incredibly rewarding, and each initiative has deepened my understanding and passion for the field.
What do you love the most about your current role?
There are several aspects of my job that I truly love. One of the most fulfilling is the tremendous business impact my team and I can achieve through data science and engineering initiatives. Seeing how data-driven decisions can significantly improve operations, drive growth, and enhance customer satisfaction is incredibly rewarding.
I also love the opportunity to transform both digital and physical shopper experiences. By analyzing consumer behavior and preferences, we can design more personalized and seamless experiences, whether online or in-store. This blend of creativity and analytical rigor keeps the work exciting and dynamic.
Additionally, the ability to deploy solutions at scale is another aspect that I find highly gratifying. Implementing a solution that not only works in a controlled environment but also performs effectively across various scenarios and locations is a challenging yet rewarding process. It involves collaboration across different teams and leveraging cutting-edge technologies, which fosters continuous learning and professional growth.
Another aspect I enjoy immensely is storytelling. The ability to take complex data and distill it into compelling narratives that stakeholders can easily understand and act upon is a powerful tool. Effective storytelling with data helps bridge the gap between technical insights and business strategy, making it easier to drive change and influence decision-making.
Where do you predict the data science market will be 5 or 10 years from now?
Five years ago, I wouldn’t have been able to predict the meteoric rise of Generative AI (Gen AI) and Large Language Models (LLMs) that have taken our industry by storm. As leaders, it’s crucial to understand and anticipate emerging trends. Back then, trends like natural language processing (NLP), improving operational cycles, deep learning and automation were critical. Similarly, I am now keenly following several key trends that I believe will shape the data science market over the next decade.
The democratization of AI, particularly through Gen AI, will make AI tools more accessible, fostering a data-driven culture within organizations. Low-code and no-code platforms will lower barriers to AI adoption. Hyper-personalization will thrive, as businesses use data to deliver tailored customer experiences, enhancing satisfaction and loyalty. MLOps (Machine Learning Operations) will become increasingly important, streamlining the deployment and maintenance of machine learning models to ensure consistent value.
The tools and techniques in data science will rapidly evolve, driven by open-source communities and academic research. Staying updated with these advancements will be crucial. Additionally, data science will increasingly address global challenges like climate change, healthcare, and social inequality, highlighting its potential for positive change.
How much focus should the aspiring data practitioners have in working with messy, noisy data? What are the other areas that they must build their expertise in?
Aspiring data practitioners should place a significant focus on working with messy, noisy data. This is because, in real-world scenarios, data is rarely clean and perfectly structured. Data Engineering domain has seen tremendous improvement lately but developing skills in data cleaning, preprocessing, and feature engineering is essential. These steps are often the most time-consuming but crucial for building accurate and reliable models. Understanding the right techniques for handling both structured and unstructured data, including text, images, and other non-tabular formats, is vital.
Additionally, aspiring data practitioners should have a broad understanding of the entire machine learning (ML) flywheel, which starts with converting business objectives into ML objectives followed by data understanding, feature engineering, model development, model deployment and experimentation. Beyond technical expertise, it’s crucial for data practitioners to build expertise in 3 main areas i.e., understanding the business context and objectives to ensure that data science efforts are aligned with strategic goals, being able to effectively communicate insights and recommendations to non-technical stakeholders through compelling narratives, and staying updated with the latest advancements in data science and machine learning, as the field is rapidly evolving.
In your academic or work career, were there any mentors who have helped you grow along the way? What’s the best piece of advice you have ever received?
I have been blessed to be surrounded by great mentors right from the start of my career and continue to engage with them regularly. These mentors have been instrumental in guiding me, helping me understand where I should focus my efforts and how to navigate the complexities of my field.
One of the best pieces of advice I received was to periodically evaluate my work to ensure it is pushing me to learn and understand new areas, aligning with my career goals, and maintaining a high speed of execution. This advice came from a mentor who emphasized the importance of self-assessment and continuous growth. He advised me to take a step back every six months to reflect on my progress and determine if I was still on the right path.
Another valuable piece of advice was to view obstacles and changes as opportunities for growth. A mentor once told me that in ever changing world of technology and data science, encountering challenges is inevitable. What matters most is how we perceive and respond to these challenges. By seeing them as opportunities to learn and innovate, I have been able to stay resilient and motivated, even when faced with setbacks.
These pieces of advice, combined with the ongoing support and guidance from my mentors, have been crucial in my career development. They have helped me take calculated risks, make informed decisions, and ensure that I am constantly growing and evolving in my field.
What, personally, has allowed you the success you have had in the role of a leader in technology?
My success as a leader in technology can be attributed to several personal qualities and experiences. One of the key factors has been my ability to embrace and drive change effectively. In the fast-paced tech industry, change is a constant, and being able to manage it efficiently has been crucial. I have developed a keen sense of how to navigate through transitions, whether it’s adopting new technologies, shifting business strategies, or restructuring teams. By fostering a culture that is open to change and encouraging flexibility, I have been able to lead my teams through various transformations smoothly.
Adaptability has also played a significant role in my success. The technology landscape is ever evolving, and being able to quickly adapt to new trends, tools, and methodologies is essential. I pride myself on staying informed about industry advancements and being open to learning and integrating new approaches. This adaptability allows me to pivot strategies when necessary and keep the team aligned with the latest innovations.
Lastly, resilience and a solution-oriented mindset have been fundamental. The ability to stay calm under pressure and approach challenges with a positive attitude has enabled me to overcome obstacles and lead my team through difficult times. By focusing on solutions rather than problems, I can inspire confidence and motivate the team to keep pushing forward.
What has been your most career-defining moment that you are proud of?
I do not believe there is just one career-defining moment. Instead, there are several notable ones that collectively define my career. One of these was building an SKU rationalization process from scratch. This project required not only deep technical expertise but also a comprehensive understanding of market dynamics and merchant needs. Seeing this evolve quickly and building a platform was incredibly rewarding.
Another significant achievement was transforming our fraud detection algorithms and processes to an industry-leading standard. This involved developing advanced algorithms and refining processes to detect and prevent fraudulent activities more effectively. The results were a marked reduction in fraud incidents and significant cost savings for the company. Additionally, I was a founding member of the team that established a multi-year strategy for personalization across the company.
What is one of your favorite parts of the workweek? How does it encourage or inspire you? Do you have a favorite way to recharge during the workday?
I keenly look forward to a couple of things during my workweek: strategy sessions and feature releases. Strategy sessions, which involve system design and team enablement, are particularly inspiring. These sessions allow me to collaborate with my team, brainstorm innovative solutions, and align our goals with the overall business strategy. The collaborative environment fosters creativity and ensures everyone is on the same page, which is incredibly motivating
Feature releases are another highlight of my week. Seeing the hard work of my team come to fruition and watching new features positively impact users is extremely rewarding. It provides a tangible sense of accomplishment and drives us to continue innovating.
To recharge during the workday, I enjoy running, that helps me refresh my mind. Additionally, spending time with my family and seeing my toddlers learn new skills every day is incredibly fulfilling and energizing. These activities not only help me unwind but also inspire me to bring my best self to work.
Where do you see yourself in the next 5 years?
I see the next 5 years as an opportunity to lead transformative change, cultivate top-tier talent, and embed data science and AI deeply into the fabric of our company, driving sustained innovation and growth.
I see myself driving change for the entire company through connected systems rather than just a few domains. This involves working closely with other C-suite executives to integrate data science and AI into every aspect of our business operations, ensuring that these technologies are leveraged to their full potential across the entire organization.
A key priority will be nurturing talent. Building and mentoring high-performing teams will remain at the forefront of my goals. I aim to develop our organization’s reputation as a top incubator of data science talent, fostering an environment where professionals can grow, innovate, and excel.
What is your advice for newbies, Data Science students or practitioners who are looking at building a career in the Data Analytics industry?
Every few years, the focus areas for new practitioners within Data Science changes with advent of new technologies and new tools at their disposal. Drawing insights from successful associates in the last few years, I would like to highlight 3 main themes starting with strong grasp on fundamentals that includes effective programming, statistics and mathematics that’ll help in doing their day to day efficiently and with high throughput. Secondly, storytelling and effective communication. The most sophisticated analysis or reasoning of need for ML model is of little use if you can’t convince others of its importance or implications. Finally, cultivating curiosity and continuous learning along with business mindset and domain knowledge. Staying curious and not getting comfortable within 1 domain but rather trying to understand different domains is crucial.