Sr. Data Scientist

Visa•Foster City, CA

2d•$156,800 - $251,100•Hybrid

About The Position

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. Visa will accept applications for this role until at least 10-12-2026. Sr. Data Scientists at the Consultant / Manager level are responsible for developing advanced data science models and leading technical project teams. The role requires hands-on experience with large-scale data sets, building scalable models, and applying machine learning and statistical techniques to solve business problems. The position involves collaborating with cross-functional teams, translating analytics output into actionable recommendations, and communicating complex concepts to diverse audiences. All roles require digital fluency, including the ability to work with emerging technologies such as Generative AI tools (e.g. ChatGPT, Microsoft Copilot) to support everyday work.

Requirements

5 or more years of relevant work experience with a Bachelor's Degree or at least 2 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 0 years of work experience with a PhD.
Experience in developing advanced data science models and applying machine learning techniques to business problems.
Experience in managing data science projects from scoping to delivery.
Experience in extracting and aggregating data from large data sets using SQL or other tools.
Experience in creating reproducible analytic pipelines.
Experience in data visualization using Tableau, Power BI, or R/Python.
Experience in working with modern distributed systems, including Hadoop, Hive/SQL, and Apache Spark.
Experience with machine learning, deep learning and LLM tools and technologies (e.g., TensorFlow, PyTorch, scikit-learn, etc.)
Experience in collaborating with cross-functional teams to deliver scalable solutions.
Experience in communicating complex technical concepts to business audiences.
Experience in leading project teams and providing technical direction.

Nice To Haves

6 or more years of work experience with a Bachelor's Degree or 4 or more years of relevant experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or up to 3 years of relevant experience with a PhD.
Extensive experience working in the data science, data engineering, or analytics profession.
Hands-on experience working with extremely large data sets and building scalable models.
Expertise in multiple programming languages (e.g., Python, R, Spark).
Experience working with real-world data sets and building scalable models from large-scale data.
Experience working in global organizations and collaborating with stakeholders across geographies.
Previous exposure to financial services or payments industry is a plus but not mandatory.

Responsibilities

Develop and implement advanced data science models, including deep learning, machine learning, recommendation systems, and generative models.
Lead and provide direction to technical data science teams, ensuring high-quality and rigorous project outcomes.
Collaborate with cross-functional teams (product, engineering, marketing) to gather requirements and deliver scalable solutions.
Translate analytics output into actionable business recommendations and communicate results to senior business audiences.
Manage end-to-end data science projects, including planning, organizing, and delivering results with diverse teams.
Work with real-world, large-scale data sets to build and deploy scalable models.
Utilize modern distributed systems and data science tools (e.g., Hadoop, Hive/SQL, Apache Spark, TensorFlow, PyTorch, scikit-learn).
Ensure reproducibility and quality in analytic pipelines and project deliverables.