Lead Data Scientist

Visa Visa · Fintech · Bellevue, WA

Lead Data Scientist at Visa, focusing on AI/ML for payment solutions. The role involves defining, building, and testing AI-powered products at scale, driving efficiencies, and ensuring operational excellence within a regulated fintech environment. Responsibilities include technical leadership, mentoring, data wrangling, complex modeling, and advising on technical specifications to shape the future of digital payments.

What you'd actually do

  1. Provides technical expertise and mentors others to implement extensible, maintainable, and reusable code, defines framework, principles, coding patterns, guidelines, styles, and standard methodologies, and adheres to all security requirements for the application of artificial intelligence and data science.
  2. Develops strategies for and leads team's efforts to drive efficiencies across data extraction and ensure data quality and completeness using data wrangling, complex data modeling, and artificial intelligence.
  3. Ensures adherence to data management principles, governance, process, and tools to maintain data quality across products.
  4. Advises on technical specifications during discussions with collaborators (e.g., Product owners, business partners, Cybersecurity) to identify and clarify sophisticated technical or business requirements and identify business needs and upstream and/or downstream system/application dependencies.
  5. Identifies complex trends across relevant data sources and uses insights to plan platform-wide future solution updates. Identifies opportunities and defines roadmap for software upgrades and server patches for security remediation where applicable.

Skills

Required

  • 10 or more years of work experience with a Bachelor’s Degree or at least 8 years of work experience with an Advanced Degree (e.g. Masters/ MBA/JD/MD) or at least 3 years of work experience with a PhD
  • Three (3) years of experience building AI / ML powered models for predictive analytics / insights
  • Four (4) years of experience solving data problems using data technologies (e.g., Hadoop, Hive, Kafka, Redis, NoSQL, RDBMS).
  • Two (2) years of experience designing, implementing, and maintaining ETL pipelines.
  • Three (3) years of Experience with data analysis, metrics building & evaluation.
  • Experience with Big Data and Analytics in general leveraging technologies like Hadoop, Spark, Flink and MapReduce.

Nice to have

  • 12 or more years of work experience with a Bachelor’s Degree or 8-10 years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 6+ years of work experience with a PhD

What the JD emphasized

  • building AI / ML powered models for predictive analytics / insights
  • meeting regulatory, security and privacy requirements

Other signals

  • AI technologies have the potential to radically transform commerce
  • Visa Intelligent Commerce to enable this next era of commerce
  • trusted, agentic experiences
  • AI transformation journey at Visa
  • innovative data science and artificial intelligence developments
  • solving complex challenges on a global scale
  • inventing, designing, building, and testing products
  • shaping the digital future of payments
  • defining, executing, and delivering product and technical features at scale
  • building for operational excellence
  • meeting regulatory, security and privacy requirements
  • leading global teams responsible for platform transformation efforts
  • continued embrace of AI
  • seeking new paths to revenue by improving delivery efficiency
  • pushing forward for new products
  • application of artificial intelligence and data science
  • drive efficiencies across data extraction
  • complex data modeling
  • artificial intelligence
  • data management principles, governance, process, and tools
  • maintaining data quality across products
  • identifying and clarifying sophisticated technical or business requirements
  • identifies complex trends across relevant data sources
  • plan platform-wide future solution updates
  • building AI / ML powered models for predictive analytics / insights
  • solving data problems using data technologies
  • designing, implementing, and maintaining ETL pipelines
  • data analysis, metrics building & evaluation
  • Big Data and Analytics in general