V

Visa

Runs a global network for digital payments

Staff Data Scientist

Applied Machine LearningPosted on Nov 13
Full-timeBellevue, WA, USA

About the Job

Requirements

  • At least 5 years of relevant work experience with a bachelor's degree, at least 2 years with an advanced degree (such as a master's, MBA, JD, or MD), no work experience with a PhD, or at least 8 years of relevant work experience.
  • Ability to work with emerging technologies such as generative AI tools, including ChatGPT and Microsoft Copilot, to support everyday work.
  • Preferred: At least 6 years of experience with a bachelor's degree, 4 or more years with an advanced degree, or up to 3 years with a PhD.
  • Preferred: An advanced degree (MS or PhD) in AI, Computer Science, Statistics, Operations Research, or a related quantitative field.
  • Preferred: Experience applying data science or analytics solutions to complex business problems with measurable outcomes.
  • Preferred: A track record of commercializing analytical or machine-learning solutions in production environments.
  • Preferred: Experience managing or leading end-to-end projects across cross-functional teams.
  • Preferred: Agile experience and ability to manage evolving priorities in dynamic environments.
  • Preferred: Experience working with large-scale datasets and building scalable models.
  • Preferred: Experience with distributed data processing frameworks such as Hadoop, Hive, or Spark.
  • Preferred: Proficiency in Python or R, along with SQL.
  • Preferred: Experience with version control tools such as Git or GitHub.
  • Preferred: Hands-on experience applying machine learning and predictive modeling techniques to business problems.
  • Preferred: Experience in data science, data engineering, or analytics roles in large-scale environments.
  • Preferred: Experience in payments, fraud, risk, disputes, fintech, or transaction-heavy domains.
  • Preferred: Experience working in global organizations with distributed stakeholders.
  • Preferred: Ability to lead projects, influence teams, and provide technical thought leadership.

Responsibilities

  • Define and lead high-impact machine-learning initiatives across dispute decisioning, merchant matching, and recovery prediction.
  • Own the end-to-end machine-learning lifecycle, including problem framing, feature engineering, model development, deployment, and monitoring.
  • Build and productionize scalable models using large-scale transaction and dispute datasets.
  • Partner with Product, Engineering, and Data Engineering to translate business needs into data-driven solutions.
  • Drive adoption of machine-learning solutions through explainability, performance measurement, and stakeholder alignment.
  • Develop experimentation frameworks to evaluate and continuously improve model performance.
  • Design and implement generative AI and large language model-based solutions for explainability, workflow automation, and decision support.
  • Influence product strategy by identifying opportunities to improve automation, efficiency, and customer outcomes.
  • Mentor junior team members and contribute to best practices across the data science organization.

About the Company

Visa

Runs a global network for digital payments

Visa runs a global payment network that connects banks, merchants and consumers so card purchases and money transfers move between financial institutions. Its VisaNet system handles authorization, clearing and settlement, linking the bank behind a buyer's card with the bank that accepts a merchant's payment. The company earns fees from payment activity, transaction processing and international transactions, and sells fraud prevention tools, payment software and consulting services to businesses. Banks and other partners issue Visa branded cards and use its technology to build their own payment products. Visa traces its roots to a Bank of America card program launched in 1958 and is headquartered in San Francisco.

Company size
10,001+
Company stage
N/A
Total funding
$24.2M
Headquarters
San Francisco, CA
Founded
1958

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