Senior Data Scientist - Network Value (Plaid App)

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam.

Senior Data Scientist - Network Value (Plaid App)

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 Senior Data Scientist supporting Plaid App, a critical user-facing product within Plaid’s Network Value portfolio. In this role, you’ll become a data and analytical thought partner to product managers and engineers, helping drive data-informed product development as the team launches and scales the product from 0 to 1. 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 metrics tied to business goals, and support feature ship decisions through experimentation. You’ll also help identify new ways Plaid App can move top-line outcomes and influence prioritization, roadmapping, and execution across cross-functional partners.

Responsibilities

Champion a data-first approach to decision-making across the organization as Plaid App is built and scaled, serving as the data and analytical thought partner to product managers, engineers, and cross-functional stakeholders.

Translate ambiguous business and product questions into clear analytics projects and decision frameworks, and perform ad-hoc and strategic analyses to identify opportunities to improve user outcomes and business performance.

Build and maintain data models, dashboards, core metrics, OKRs, and success metrics that improve visibility into Guard performance and quantify progress against business goals.

Design and analyze experiments to support feature launch and iteration decisions, and partner on Airflow- and dbt-powered data pipelines that enable trustworthy analytics and scalable reporting.

Identify novel ways to impact top-line OKRs and influence stakeholders on prioritization, roadmapping, and execution, while exploring ML prototypes and causal inference approaches where they can improve product or decision quality.

Qualifications

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

Fintech experience, including experience working with raw fintech data

Experience launching direct-to-consumer products from 0 to 1

Experience with experimentation, ad-hoc analysis, and developing strategic insights

Strong SQL skills and experience creating metrics that drive alignment with stakeholders

Experience building or partnering closely on data pipelines using Airflow and dbt

Strong communication skills and experience partnering cross-functionally with product managers, engineers, and other stakeholders

Experience driving data-driven performance for user-facing products

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

Nice to have

Experience with causal inference

Experience with machine learning

Python proficiency