From Tinkering to OpenAI: The Journey of Data Scientist Sarang Gupta

In the rapidly evolving landscape of generative artificial intelligence, the gap between a powerful model and a successful business application is often where the most critical operate happens. While many engineers focus on the theoretical limits of large language models, OpenAI engineer Sarang Gupta is focusing on the practicalities of growth, helping companies bridge that divide to attract more buyers and boost sales.

Based in San Francisco, Gupta serves as a member of the data science staff at OpenAI, where he operates at the intersection of high-level engineering and corporate strategy. His role is centered on the “go-to-market” (GTM) team, a pivotal division tasked with ensuring that ChatGPT and other OpenAI products are not just technically viable, but strategically adopted by businesses globally.

By building data-driven models and systems that support sales and marketing divisions, Gupta is effectively translating the raw power of AI into a language that businesses can employ to scale. His work focuses on developing models to understand the efficiency of various marketing channels and measuring what specifically drives impact, allowing the company to better reach and serve its diverse customer base.

A Foundation in Tinkering and Business Logic

Gupta’s approach to AI is rooted in a lifelong habit of tinkering. By age 11, he had moved from repairing household items to learning programming languages such as Basic and Logo. This early curiosity led to his first foray into business automation, where he designed simple programs to help a local restaurant automate its billing and online ordering systems.

This dual interest in the “how” of engineering and the “why” of business became the blueprint for his academic career. After graduating high school in 2012 from the Chinmaya International Residential School in Tamil Nadu, India, he pursued a dual bachelor’s program at the Hong Kong University of Science and Technology. Over four years, he earned degrees in both industrial engineering and business management, graduating in 2016.

During his time in Hong Kong, Gupta experimented with the practical application of software, developing a smartphone app to help students coordinate lunch schedules and launching a niche advertising business called Pulp Ads, which printed advertisements on tissues and napkins for school cafeterias. While these ventures were small-scale, they established his preference for technology that solves real-world problems.

Optimizing Workflows from Wall Street to Journalism

Before entering the AI field, Gupta applied his optimization skills to the financial sector. He joined Goldman Sachs as an analyst in the operations division, where he focused on identifying bottlenecks in securities transaction workflows. He specifically targeted trade reconciliation—the manual process of comparing data across spreadsheets to ensure consistency.

To solve this, Gupta built internal automation tools that pulled trade data from multiple systems, ran validation checks, and flagged only the discrepancies that required human investigation. This experience provided his first significant exposure to how data systems could dramatically improve operational efficiency at scale.

Driven by a desire to deepen his technical expertise, Gupta returned to academia in 2018. He enrolled in a dedicated master’s degree program in data science with a focus on AI at Columbia University, graduating in 2020. During his studies, he gravitated toward applied machine learning, deep learning, and neural networks.

One of his most impactful academic projects took place in 2019 through the Brown Institute, a joint research lab between Columbia and Stanford. Working with The Philadelphia Inquirer, Gupta and his team built tools to extract locations from news articles to visualize coverage gaps. This project identified “news deserts”—underserved communities receiving little to no reporting—enabling the newsroom to redirect its resources to those areas.

The Shift to Generative AI and Asana Intelligence

After completing his master’s degree, Gupta moved to San Francisco to join Asana as a product data scientist. His initial focus was on A/B testing for new platform features, but his trajectory shifted when he was asked to lead the launch of “Asana Intelligence,” an internal machine learning team.

Given a six-month window, Gupta’s team developed several AI-powered features designed to increase customer efficiency. Among these was “Smart Status,” a tool that analyzes project tasks, deadlines, and activity to automatically generate status updates. The success of these features led Gupta and his colleagues to file several U.S. Patents for their machine learning frameworks.

The mainstreaming of generative AI and the launch of ChatGPT shifted the industry’s focus from traditional model development to the assessment of Large Language Models (LLMs). Recognizing the potential of this inflection point, Gupta left Asana in September 2025 to join the data science team at OpenAI.

Scaling AI’s Benefits Globally

Now at OpenAI, Gupta views the current competitive environment as an opportunity for rapid learning. He emphasizes that the pace of the industry requires a strong expectation to deliver fast, particularly when guiding strategic decisions for the marketing team.

Beyond the corporate metrics, Gupta maintains a personal goal to ensure that the benefits of AI reach as many people as possible. He advocates for the potential of task automation across all industries, noting that AI has already improved his own professional life by helping him frame his communication and present his work more clearly.

As an IEEE senior member since 2024, Gupta continues to utilize the IEEE Xplore Digital Library and professional networks to stay current on the evolution of data science and engineering. For Gupta, the ultimate value of AI lies in its ability to drive efficiencies for businesses while simultaneously helping individuals improve their own capabilities.

Key Career Milestones

As OpenAI continues to expand its enterprise offerings, the work of engineers like Sarang Gupta will be essential in determining how AI is integrated into the global economy. The focus is shifting from what these models can do to how they can be most effectively deployed to drive measurable business growth.

We will continue to monitor OpenAI’s strategic shifts in enterprise adoption and the rollout of new GTM-driven features. Do you think AI-driven marketing models will replace traditional sales strategies, or simply enhance them? Share your thoughts in the comments below.