Data Scientist - Network Value

PlaidSan Francisco, CA

About The Position

The Network Value Data Science team is helping Plaid build an industry leading fintech consumer network by increasing access to, authorization for, and usability of Plaid’s User’s financial footprints. We embed within product teams to support OKRs and help execute on product roadmaps. We translate ambiguous product questions into tractable analysis, serve as analytical thought partners throughout the org, identify opportunities to build better products, and champion a data-first decision making approach everywhere we go. You’ll be a Data Scientist supporting Network Enablement Access (NEA). In this role, you’ll help build an industry-leading fintech consumer network by expanding access to, authorization for, and usability of Plaid users’ financial footprints. You’ll partner closely with embedded teams to support product goals and roadmaps, ensuring Plaid continues to deliver trusted, innovative, and scalable solutions that empower consumers to connect and use their financial data with confidence.

Requirements

  • 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

Responsibilities

  • Perform ad-hoc and strategic analyses to uncover opportunities for improved business outcomes and translate complex questions into actionable analytics projects.
  • Design and maintain scalable data models and dashboards that increase visibility into core systems and drive operational excellence.
  • Build and iterate on machine learning prototypes to power insight-driven products and unlock new sources of customer and business value.
  • Define and track OKRs that quantify progress toward key business goals, ensuring alignment and accountability across teams.
  • Design and analyze experiments to guide product decisions and optimize feature launches.
  • Champion a data-first culture by promoting analytical rigor and evidence-based decision-making across the organization.
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