Senior Data Scientist

Mastercard Mastercard · Fintech · Salt Lake City, UT +1 · AI & Data

Mastercard is seeking a Senior Data Scientist to develop and drive data-driven open banking solutions, focusing on affordability/credit decisioning and identity/income verification. The role involves applying data science techniques to large datasets, building interactive dashboards, supporting customer trials, and contributing to team methods for prototyping and governance. Experience with time series, NLP, and solution deployment is required, preferably within financial services.

What you'd actually do

  1. In pursuit of highly valued, market leading, solutions and insights, apply a range of problem appropriate data science techniques to large data sets, from development to deployment support.
  2. Work closely with data engineers and developers to build and deploy interactive dashboards, providing the best, most engaging insights and UX for our clients.
  3. Communicate effectively with clients and stakeholders, ensuring their requirements are fully understood and met.
  4. Conduct effective customer trials to grow our open banking impact, supporting data specification, data processing/analysis and result generation/presentation.
  5. Be highly proactive in pursuit of product excellence. For example, by investigating/proposing new data sources, encouraging cross team working, managing projects to agreed schedules and looking to utilise new tools and techniques.

Skills

Required

  • Python coding experience
  • Python Data Science / Machine Learning (ML) library ecosystem
  • Plotly’s Dash framework
  • time series
  • natural language processing (NLP)
  • topic modelling
  • named entity recognition
  • supervised and un-supervised techniques
  • solution deployment
  • data science pipelines
  • MLOps frameworks and libraries
  • financial services experience
  • consumer and/or business lending

Nice to have

  • open banking solutions
  • affordability/credit decisioning
  • identity/income verification
  • customer trials
  • cross team working
  • team methods
  • rapid prototyping
  • reproducibility
  • productivity
  • automation
  • data governance
  • wider Mastercard data science community

What the JD emphasized

  • end-to-end data science process
  • solution deployment
  • data science pipelines
  • MLOps frameworks and libraries

Other signals

  • develop and drive forward Mastercard’s ambitious, data-driven open banking solutions
  • apply a range of problem appropriate data science techniques to large data sets, from development to deployment support
  • develop, implement and honour effective, engaging team methods to support rapid prototyping, reproducibility, productivity, automation, and appropriate data governance
  • investigating/proposing new data sources
  • utilize new tools and techniques