Staff ML Scientist

About Us

Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid.

At Visa, you'll have the opportunity to create impact at scale — tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world.

Join Visa and do work that matters – to you, to your community, and to the world. Progress starts with you.

Job Description

We are looking for a Staff ML Scientist to design and develop next‑generation AI and ML models that solve real‑world challenges in the payments ecosystem covering transaction monitoring, fraud detection, and risk mitigation across multiple Visa businesses. You will be part of cross-functional team of data scientists and ML engineers based out of Bangalore and collaborating globally, driving innovation in the Risk and Identity Solutions (RaIS) through advanced AI/ML technologies.

You will combine deep technical expertise with ownership, velocity, and influence. You will drive the scientific roadmap at scale, ensuring models are aligned, explainable, and robust, and you’ll collaborate with world‑class experts while influencing product strategy in a high‑stakes domain. The position offers access to top‑tier compute, tools, and datasets, and a collaborative, innovative culture focused on shaping the future of global payments.

Responsibilities:

- Extract insights from large-scale Visa datasets across business lines to define high-value, fit-for-purpose problems beyond payments - driving clarity and framing business impact with precision.

- Fine-tuning open-weight LLMs to reduce cost – bring deep expertise in parameter-efficient fine-tuning (LoRA/QLoRA) and distillation to achieve near frontier performance at significantly lower inference and training cost.

- Classifier as an LLM model - frame classification problems (e.g., fraud signals, intent detection) as generative tasks using LLMs, enabling better generalization and faster iteration vs. traditional pipelines.

- Lead other ML PoCs - risk scoring of stablecoins, intent misalignment, and merchant discoverability for agentic commerce. Rapidly prototype and validate new problem spaces with lightweight experimentation frameworks to identify high-ROI opportunities for scaling.