By the DSC Next Editorial Desk :
Ahsaas Bajaj is the kind of machine learning leader who operates confidently at the intersection of rigorous
research and real-world impact. Over the past several years, he has built and scaled production machine
learning systems that meaningfully improve customer experience while delivering measurable business
outcomes. He currently serves as Senior Machine Learning Engineer II on Instacart’s shopping ML team,
where his work has shaped how millions of customers receive high-quality product replacements when
items are out of stock — one of the most complex and customer-sensitive challenges in online grocery.
At Instacart, Ahsaas has been instrumental in inventing and deploying personalized replacements
machine learning systems that have generated more than $17 million in cumulative incremental
contribution profit and influenced over $200 million in incremental gross transaction value. His work has
been significant enough to be referenced in Instacart’s public shareholder communications. Beyond
financial impact, these systems have improved core customer experience metrics, including sustained
customer satisfaction rates above 95 percent, while supporting more than 300 million requests annually.
A defining aspect of Ahsaas’s work is his ability to transform individual models into durable, scalable
platforms. He designed a real-time, multi-surface ranking system that powers substitution flows across
both customer-facing and shopper-facing experiences. This platform delivered step-change
improvements in 2025 alone, driving millions of dollars in additional profit and significantly reducing
disapproved replacements. In practice, he has also served as the de facto technical lead for
replacements machine learning, defining roadmaps, experimentation standards, and observability
practices adopted across multiple teams.
Before joining Instacart, Ahsaas worked as a Data Scientist at Walmart Labs, where he focused on
large-scale personalization and recommendation systems for the grocery experience. His work improved
the customer repurchase journey, increased diversity in top recommendations, and delivered measurable
gains in basket prediction accuracy by modeling customer affinity across product categories.
Earlier in his career, Ahsaas gained hands-on experience building production natural language
processing systems. As a Software Engineering Intern at Instabase, he delivered BERT-based entity
recognition and table question-answering systems using modern infrastructure such as Kubernetes and
gRPC. At Samsung R&D; Institute in Bangalore, he contributed to the commercialization of an on-device
unified search engine deployed on flagship smartphones, improving performance and recall at massive
scale.
In addition to his industry work, Ahsaas is an active inventor and researcher. He holds a granted U.S.
patent from Walmart related to adapting graphical user interfaces based on user affinity and repurchase
intent, as well as published patents at Instacart covering machine-learning- and language-model-driven
product replacements. His academic research has been published in leading venues including
ACL-IJCNLP, EMNLP, and NLDB, and his work has received over one hundred citations.
Ahsaas also contributes extensively to the broader research community. He serves as a program
committee member and invited reviewer for top-tier conferences such as AAAI, KDD, UMAP, and ACL-affiliated venues, evaluating research across large language models, personalization, and applied
machine learning.
His contributions and leadership have been recognized through multiple awards, including Instacart’s
Individual Tech Achievement Award and Samsung’s Citizen Award. He is also a member of the Forbes
Technology Council, an invitation-only community recognizing senior technology leaders for professional
excellence and industry impact.
Ahsaas holds a Master of Science in Computer Science from the University of Massachusetts Amherst,
where he graduated with a perfect GPA, and a Bachelor’s degree in Electronics and Communication
Engineering from the University of Delhi, where he ranked near the top of his cohort. His technical
expertise spans the full machine learning lifecycle, from research and modeling to system design,
deployment, and long-term ownership.
As a committee member, Ahsaas brings a rare combination of deep technical rigor, production
experience, and product intuition. His work reflects a consistent focus on building machine learning
systems that scale responsibly, perform reliably under real-world constraints, and deliver outcomes that
genuinely matter to users and organizations alike.
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