As Data Science technologies mature, there will be a transition towards more responsible AI systems with a focus on models and pipelines that are transparent, reliable, and fair in their predictions, helping ensure that decisions made by these systems can be trusted at scale.
Ieshika Chandra, our next pathbreaker, Data Scientist at Walmart Global Tech, builds analytics and machine learning systems that improve product discovery and enable faster, data-driven decision-making across the organization.
Ieshika talks to Shyam Krishnamurthy from The Interview Portal about the moment she realized that with the right models and analysis, data can act like a compass, guiding companies towards more responsible decisions that can shape a greener world.
For students, Technology and AI are becoming part of everything we do, and it’s important to stay curious and adapt.
Ieshika, what were your growing up years like?
I grew up in a family where curiosity and learning were a natural part of life. My mom was a teacher, and she had this wonderful habit of reading about everything- science, technology, food, and health. Watching her stay curious and excited about learning inspired me to do the same.
As a child, I was always asking questions and exploring new ideas. I took part in sports like basketball, debates, poems, and even loved music. Music taught me patience and discipline, while debates helped me think clearly and communicate well. Even something as simple as grocery shopping fascinated me. I would notice patterns, think about why some products were placed in certain ways, and observe how people made choices. Looking back, all these interests shaped how I think today as a data scientist, because data is essentially about patterns and understanding behavior.
Another big influence was my grandfather. He was a doctor and one of the most humble, generous people I’ve ever known. He always helped others and lived with a sense of purpose. From him, I learned that knowledge is most valuable when it is used to make a positive difference in people’s lives.
What did you do for graduation/post-graduation?
For my bachelor’s degree, I studied Electronics Engineering with a focus on digital systems. I really enjoyed the technical side, but what fascinated me the most was working with data and writing code to solve problems. I loved figuring out patterns, predicting outcomes, and understanding how technology could be used to make better decisions.
After that, I pursued a Master’s in Data Science at the University of Connecticut, where I focused on Predictive Modeling and Machine Learning. I learned how to use algorithms to make predictions, identify patterns in large datasets, and build models that could help organizations make smarter decisions. For example, I learned how to train models to forecast trends, classify information, and recommend actions based on data.
This education gave me the foundation to tackle complex problems at scale, which is critical for my work today. It also shaped my long-term vision for using AI and analytics responsibly. I realized that data science isn’t just about coding or math, it’s about creating systems that can make fair, transparent, and trustworthy decisions.
What were some of the key influences that led you to a career in Data Science?
I was inspired by how powerful data can be, it’s like oil: the more you explore it, the more valuable insights you can uncover. I remember watching Andrew Ng’s machine learning lectures. At first, they were very difficult, but I kept reading, rewatching, and practicing until I understood. That persistence taught me a lot about learning and problem-solving.
Family also played a big role. My mom’s curiosity motivated me to keep learning and exploring new ideas. It shaped the way I approach my career with curiosity, persistence, and a focus on making a positive impact.
Tell us about your career path.
My approach has always been to learn, apply, and grow, with each step building on the previous one.
While doing my engineering, I worked at BARC on predictive modeling for telescope data, where I integrated a Multi-Channel Coincidence Monitor for gamma ray detection and built logistic regression models to predict the probability of coincident events. It was a conscious choice to merge my engineering background with data analysis, and it gave me an early exposure to using modeling to extract insights from complex scientific data.
I also spent two years at Accenture before my master’s, where I worked on technology and consulting projects. I gained experience solving large-scale problems, collaborating with business and technical teams, and understanding how data and systems drive enterprise decisions. This experience motivated me to pursue a master’s, so I could deepen my expertise in analytics, machine learning, and scalable data systems to tackle more complex problems.
After my master’s, I joined EY in Data Science, where I worked on sustainability analytics, supply chain optimization, and financial compliance. For example, I built predictive models to forecast demand, optimize inventory, and reduce waste, and developed tools to track carbon footprints and detect anomalies in financial systems. During COVID-19, I analyzed loan data to detect potential fraud and ensure resources were allocated appropriately. These experiences taught me how to apply advanced analytics, machine learning, and scalable pipelines to solve real-world problems with tangible societal and economic impact.
My next stint was at Shutterstock which is a global content platform that provides stock images, videos, and music to individuals and businesses. From a data science perspective, the focus is on understanding customer behavior and improving engagement. I worked on retention models to reduce cancellations and refunds and also analyzed product usage to design strategies for cross-selling and up-selling. So, the data science work there was about keeping customers engaged and helping the business grow.
I later moved to Lyft, a ride sharing company, where I focused on Data Science experimentation and product growth. I collaborated with engineers and product managers to design rigorous A/B tests, analyze user behavior, and launch new features. This role allowed me to see how advanced analytics and machine learning can directly improve experiences for millions of users, while also strengthening my expertise in building scalable data systems and translating insights into actionable decisions skills that are central to my ongoing work in AI-driven digital marketplace infrastructure.
At Lyft, I worked on the Garage team, focused on customer acquisition and launching new products. I helped design the key metrics to measure adoption and ran experiments that improved product usage and reduced cancellations, making the new services more reliable for customers. On the marketing side, I built multi-channel attribution models to understand which campaigns and channels were most effective, which helped the team allocate resources more wisely. It was a great example of how data science can support both product growth and marketing strategy in very different ways.
Currently, I work at Walmart as Manager of Advanced Analytics and Data Science. I lead projects that improve product discovery, experimentation, and large-scale analytics infrastructure. I develop machine learning models and automated reporting systems that help teams make faster, more accurate decisions. These systems ensure that data is reliable, and decisions are evidence based, which is critical for innovation at such a large scale.
How did you get your first break?
My first break came at EY, right after finishing my Master’s. One of my first projects was helping large organizations meet their sustainability goals and reduce emissions across Scope 1, 2, and 3, covering direct emissions, energy use, and emissions across the supply chain.
I got to use data science and machine learning in a very hands-on way. We collected massive amounts of data from different sources like energy usage, transportation, and production logs and cleaned it so it could be analyzed. Then I built predictive models that could estimate emissions and suggest the best ways to cut them. For example, the models could show how changing suppliers or optimizing transportation could reduce emissions significantly.
What made this project so exciting was seeing how numbers and algorithms could guide real-world decisions. It felt like translating data into a roadmap for companies to take concrete steps toward a greener future. I realized that with the right models and analysis, data can act like a compass, it points the way to smarter, more responsible decisions that have real impact on the world.
What were some of the challenges you faced and how did you address them?
One challenge I faced early on was finding a job in the US while I was still in my Master’s program. Navigating applications, interviews, and the competitive job market was stressful, but persistence, preparation, and networking paid off and I landed a role two months before graduation, which was a huge relief and gave me a big confidence boost.
Another challenge was adapting to US work culture, which emphasizes open communication and collaboration. At first, it felt different from what I was used to, but I learned to ask questions confidently, share my ideas openly, and work effectively with diverse teams.
The third challenge was keeping up with the rapid pace of technology. Data Science evolves incredibly quickly, and new algorithms, tools, and frameworks emerge all the time. To stay ahead, I made continuous learning a habit by taking online courses, reading research papers, and experimenting with new techniques. I also contribute to the ML Commons community, which promotes responsible and scalable machine learning, and became actively involved with the Women in Data Science (WiDS) community, mentoring and supporting other learners. These experiences not only strengthened my technical skills but also taught me the value of sharing knowledge and building a supportive, inclusive community in the world of data science.
Where do you work now? What problems do you solve?
I currently work at Walmart, one of the largest retailers and digital commerce platforms in the US, in the Decision Science domain. My work focuses on improving product discovery, designing experiments for new features, and building analytics and machine learning systems that enable fast, data-driven decision-making across the organization.
I develop scalable machine learning models that personalize user experiences, create automated dashboards and reporting pipelines, and ensure that all data is accurate and reliable. This infrastructure allows Walmart to innovate quickly, make smarter business decisions, and provide a smoother, more seamless shopping experience for millions of customers.
I work on problems for both external and internal customers. For external customers, I develop machine learning models and analytics systems that personalize the shopping experience, improve product discovery, and make it easier for millions of shoppers to find what they need. For internal customers, I build automated dashboards, reporting pipelines, and data infrastructure that help teams across Walmart make fast, data-driven decisions. This combination ensures that our analytics work both improves the customer experience and supports smarter business decisions internally.
What do you love about your work?
What I love most about my job is seeing how data-driven solutions can directly impact people’s experiences while also improving the efficiency of a massive organization. Every model, experiment, or dashboard I build helps teams make better decisions faster, which ultimately benefits both the business and its customers.
What are the skills needed for your role? How did you acquire them?
The skills needed for this work include Python/SQL programming, statistical analysis, experimentation, machine learning, and data visualization. I developed these skills through my Master’s education and over 10 years of experience working in data science roles across Fortune 100 companies. Beyond technical skills, this role also requires critical thinking, collaboration, and a focus on building systems that are fair, transparent, and responsible, principles that guide all my work with AI and large-scale analytics.
How does your work benefit society?
My work helps people make better decisions and creates smoother, more reliable experiences for millions of users on digital platforms. For example, the machine learning recommendation systems I develop help people find the right products faster, improving convenience and satisfaction. Automated analytics pipelines and reporting systems allow teams to make faster, more informed decisions, increasing operational efficiency and ensuring reliable services.
Beyond everyday applications, my work has contributed to broader societal benefits. In financial compliance and AML projects, I designed AI-driven data pipelines and anomaly detection models to identify suspicious activities, protecting consumers and maintaining the integrity of financial systems. During COVID-19, I also helped develop systems that detected potential fraud in critical programs, ensuring resources reached the right recipients and supporting communities in need.
I have also made progress toward building more responsible AI systems by developing models and pipelines that are transparent, reliable, and fair in their predictions, helping ensure that decisions made by these systems can be trusted at scale.
Can you share one memorable piece of work that is very close to you?
One of the most memorable projects I’ve worked on was in financial compliance and anti-money laundering (AML). The goal was to detect suspicious activities and prevent fraud in critical banking programs, including initiatives during COVID-19. I built AI-driven data pipelines and anomaly detection models that could flag unusual transactions and assess risk at scale.
What made this project very close to me was seeing how data science could directly protect people and communities. By identifying potential fraud, we ensured that resources went to the right recipients and helped maintain trust in the financial system. I also had to work closely with different teams, translating complex models into actionable insights that non-technical stakeholders could use.
This project taught me that data science isn’t just about algorithms, it’s about using technology responsibly to make systems safer, fairer, and more reliable. It was a defining experience because it combined technical challenge, teamwork, and real-world impact in a way I had never seen before.
Your advice to students based on your experience?
Never stop learning. Technology and AI are becoming part of everything we do, and it’s important to stay curious and adapt. When I first started learning machine learning, it was difficult, but I kept practicing, rewatching lectures, and reading until I understood. Persistence pays off.
Always ask questions, explore your interests, and don’t be afraid of challenges. The skills you build now will shape your future opportunities.
Future plans?
Looking ahead, I want to focus on making AI and data systems more responsible, fair, and transparent. For example, I am interested in building ranking and recommendation systems that ensure fairness, so that all products and features are evaluated and presented accurately, without bias.
I also want to expand into real-time analytics infrastructure and experimentation frameworks, which will help organizations make faster, smarter decisions. Above all, I aim to continue advancing ethical AI practices, ensuring that technology is safe, trustworthy, and beneficial for everyone who uses it.