Tanika Gupta

Tanika Gupta

Director Data Science

Sigmoid

Tanika Gupta is a seasoned Generative AI leader with over 13 years of expertise in spearheading AI innovation, shaping product strategy, and executing large-scale implementations across finance, technology, and consumer goods. As the Director of Data Science at Sigmoid, she spearheads multiple ML and Gen AI initiatives, driving innovation and measurable business outcomes. Previously, she served as Vice President of Machine Learning at JP Morgan Chase, leading the Fraud Modeling team for consumer cards. 

With extensive expertise in AI product development, scalable machine learning solutions, and strategic technical leadership, Tanika has built and led high-performing AI teams, filed multiple patents, and won industry-recognized AI hackathons, demonstrating her ability to drive innovation from ideation to execution.

Beyond her technical expertise, she is an influential speaker at global AI conferences, a mentor in the AI community, and an advocate for women in AI and data science.

Software development is entering a new era where creativity, not just coding skills, drives innovation. With the rise of AI-powered coding assistants, "vibe coding" is transforming how we build from writing every line manually to collaborating seamlessly with AI. This session dives into the emerging practice of vibe coding, where describing ideas and guiding AI replaces traditional programming workflows. Explore how advancements in large language models, AI code generation, and natural language interfaces are reshaping software creation. Discover how developers are leveraging AI tools to build faster, prototype effortlessly, and unlock new possibilities with minimal friction. To bring these ideas to life, I will also showcase a live demo on how you can build real applications using vibe coding techniques.

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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More

Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More
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