Machine Learning Scientist - Staff level

Visa•Austin, TX

1d•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.

Requirements

5+ 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, OR 8+ years of relevant work experience.
5+ years of relevant experience with a Bachelor’s degree, or 3+ years with an advanced degree.
Degree in Computer Science, Machine Learning, Statistics, Applied Mathematics, or a closely related field.
Proven experience designing and deploying machine learning models in production.
Strong foundation in statistics, probability, linear algebra, and optimization.
Expertise in algorithms, data structures, and large-scale data processing.
Proficiency in Python and common ML libraries (e.g., NumPy, pandas, scikit-learn, PyTorch, TensorFlow).
Experience working in Agile environments and collaborating with cross-functional teams.
Demonstrated analytical rigor, strong problem-solving skills, and independent technical judgment.

Responsibilities

Design, develop, and deploy machine learning and statistical models for large-scale, real-time and batch payment systems.
Apply advanced techniques in supervised, unsupervised, and semi-supervised learning, including deep learning, anomaly detection, and graph-based models.
Partner with data engineers and platform teams to build scalable ML pipelines for training, inference, monitoring, and retraining.
Collaborate with product managers and domain experts to define ML-driven solutions aligned with business objectives.
Conduct exploratory data analysis, feature engineering, and model experimentation using large, complex datasets.
Evaluate model performance using robust offline metrics and online experimentation (A/B testing).
Drive improvements in model accuracy, latency, explain ability, robustness, and fairness.
Contribute to architectural decisions related to ML platforms, model serving, and real-time decision systems.
Mentor junior scientists and engineers in ML best practices, experimentation rigor, and applied research.
Stay current with emerging research and apply relevant advances in machine learning, AI, and data science to production systems.
Translate ambiguous business problems into well-defined ML formulations and solution approaches.
Own the end-to-end ML lifecycle: problem definition, data preparation, modeling, evaluation, deployment, and monitoring.
Produce high-quality technical documentation explaining model design, assumptions, and trade-offs.
Collaborate across engineering, product, architecture, risk, and operations teams to ensure successful adoption of ML solutions.
Provide technical leadership in model reviews, experimentation design, and production readiness assessments.
Support production models, including troubleshooting performance degradation and participating in on-call rotations as needed.
Ensure models meet Visa’s standards for security, privacy, compliance, and ethical AI.