FaireSan Francisco, CA$211k-$291k2h ago
VisaPosted 1mo ago
Staff ML Scientist - PFI at Visa scores 93 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.
AI in this role
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
Essential Functions:
- Proficient in exploratory data analysis (EDA) using Python’s scientific libraries including NumPy, Pandas, Matplotlib, Seaborn, and scikit‑learn.
- Strong development experience in Python.
- Hands‑on experience building, training, testing, validating, and productizing machine learning models for high‑performance use cases.
- Solid understanding of core machine learning concepts, including feature engineering, model evaluation, and optimization.
- Experience implementing MLOps best practices, including model versioning, monitoring, and CI/CD pipelines for ML models.
- Hands‑on experience with AWS SageMaker for building, training, tuning, and deploying ML models.
- Hands‑on experience with to AWS services for machine learning workloads, such as S3, EC2, ECR, EKS, Lambda, CloudWatch.
- Strong understanding of model explainability frameworks such as SHAP, and the ability to interpret and explain model behavior.
- Experience debugging and analyzing false positive and false negative cases, including supporting client or production issues.
- Hands‑on experience and solid understanding of deep learning models, with exposure to frameworks such as TensorFlow, PyTorch, or Keras.
- Strong problem‑solving skills with the ability to move beyond tasks and propose improved or alternative solutions.
- Experience with ML lifecycle management and experimentation frameworks such as MLflow
This is a hybrid position. Expectation of days in office will be confirmed by your hiring manager.

Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager.Qualifications
8+ yrs. work experience with a Bachelor’s Degree or 6+ years of work experience with a Master's or Advanced Degree in an analytical field such as computer science, statistics, finance, economics or relevant area. Additional Skills (Plus) Exposure to model serving and inference engines such as TensorFlow Serving, Triton Inference Server, or similar technologies. Experience building and maintaining Spark‑based data and feature pipelines to support ML training and inference workflows. Familiarity with big data platforms and storage systems such as Hadoop, EMR, and NoSQL databases.Visa is an EEO Employer
Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How do you monitor a model once it's live, and how do you know it needs retraining?
- How do you decide that one model's output is better than another's for a given task?
- What are the limits of PyTorch that you've run into, and how did you work around them?
- What's a project where you used TensorFlow hands-on?
- Walk me through how you've used Keras in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: Ml Ops, AI Evaluation, PyTorch, TensorFlow, and Keras. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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