Data Scientist

SENIOR DATA SCIENTIST - NETWORK VALUE (CREDIT)

The Network Value Data Science team is helping Plaid build an industry-leading fintech consumer network with best-in-class products and user experiences. We are a product analytics team embedded in key product areas across Plaid. We support some of Plaid’s most important 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 Credit, a critical product area within Plaid’s Network Value portfolio. You’ll become a data and analytical thought partner to product managers, engineers, and cross-functional stakeholders, helping shape Credit product strategy, improve product performance and user experience, and grow Plaid’s consumer network. You’ll translate business questions into analytics projects, perform ad-hoc and strategic analysis, improve visibility into core systems through data modeling and dashboarding, create OKRs and KPIs tied to business goals and user experiences, and support feature-shipping decisions through experimentation. You’ll also work with SQL, Python, Redshift, Databricks, notebooks, dbt, and Airflow to enable trustworthy analytics and scalable reporting.

Today, Plaid’s Credit business primarily supports income and asset verification solutions. As cash flow data becomes an increasingly important tool across the lender lifecycle - from acquisition through servicing - you’ll help build cash-flow-based products that enable lenders to approve more borrowers, reduce losses, and reach new segments.

WHAT EXCITES YOU

- Champion a data-first approach to decision-making across Plaid and help teams use evidence to set direction.

- Partner closely with product managers, engineers, and other stakeholders to define problems, shape product strategy, and execute against roadmaps.

- Translate ambiguous business and product questions into clear analytics projects, decision frameworks, and measurable outcomes.

- Perform ad-hoc and strategic analyses that identify opportunities to improve product performance, user experiences, and business results.

- Build and maintain data models, dashboards, core metrics, OKRs, and KPIs that improve visibility into Credit’s systems and quantify progress against goals.

- Design and analyze experiments that inform feature launches, iteration, and ship decisions.

- Partner on dbt- and Airflow-powered data pipelines and use SQL, Python, Redshift, Databricks, and notebooks to create reliable, scalable analytics.

- Identify novel ways to influence top-line OKRs and help stakeholders make thoughtful prioritization, roadmapping, and execution decisions.

- Over the next year, shape Credit product strategy, improve product performance and user experience, and contribute to growth of Plaid’s consumer network.

WHAT EXCITES US

MUST-HAVE QUALIFICATIONS

- 5–8+ years of experience as a Data Scientist or in a related analytics or data-focused role.

- Experience as a product data scientist helping grow an early-stage or consumer-facing product, ideally from 0 to 1.

- Experience with experimentation, ad-hoc analysis, and strategic insight generation in a product environment.

- Strong SQL skills and experience creating metrics that drive alignment and decision-making with stakeholders.

- Experience driving data-informed performance improvements for user-facing products.

- Experience building or partnering closely on data pipelines using tools such as Airflow and dbt.

- A track record of identifying novel ways to impact a top-line OKR and influencing stakeholders on prioritization, roadmapping, and/or execution.

- Strong communication skills and the ability to explain analytical methods, tradeoffs, and recommendations to product managers, engineers, and other cross-functional partners.

NICE-TO-HAVE QUALIFICATIONS

- Fintech experience, including experience working with raw fintech or financial transaction data.

- Experience with causal inference or machine learning.

- Python proficiency.