Data Scientist - Network Value

Plaid · Fintech · San Francisco, CA · All Cost Centers

Data Scientist role at Plaid focused on expanding access, authorization, and usability of user financial footprints within a fintech consumer network. The role involves performing analyses, designing data models, building ML prototypes, defining OKRs, and analyzing experiments to drive product and business outcomes. Requires strong SQL, Python, and experience with data pipelines and ML prototyping.

What you'd actually do

  1. Perform ad-hoc and strategic analyses to uncover opportunities for improved business outcomes and translate complex questions into actionable analytics projects.
  2. Design and maintain scalable data models and dashboards that increase visibility into core systems and drive operational excellence.
  3. Build and iterate on machine learning prototypes to power insight-driven products and unlock new sources of customer and business value.
  4. Define and track OKRs that quantify progress toward key business goals, ensuring alignment and accountability across teams.
  5. Design and analyze experiments to guide product decisions and optimize feature launches.

Skills

Required

  • 2+ years of experience as a Data Scientist or in a related analytics or data-focused role
  • Strong track record of turning complex data into strategic insights and measurable business impact
  • Proven ability to use experimentation, advanced analytics, and data storytelling to uncover opportunities that drive key product and business outcomes
  • Strong technical foundation in SQL and Python for large-scale analysis, data modeling, and ML prototyping
  • Experience developing and maintaining data pipelines and metrics frameworks using tools such as Airflow and dbt
  • Background working with complex backend systems, ensuring data integrity, scalability, and operational reliability across platforms
  • Skilled at partnering cross-functionally with product, engineering, and business teams to influence prioritization and strategy through clear, data-driven communication

What the JD emphasized

  • machine learning prototypes

Other signals

  • Build and iterate on machine learning prototypes
  • translate complex questions into actionable analytics projects
  • Design and analyze experiments