Experienced Data Scientist - Network Insights

Job Description

• The Data Science team is responsible for serving Plaid’s product analytics, experimentation and modeling needs. • Making data driven decisions is key to Plaid's culture. To support that, we need a data science team that can apply their analytical skills to understand our users and influence decision making. This includes: building and maintaining core data sets and metrics, helping the team design experiments and design experiments and running in-depth follow up analyses, and supporting, educating, and providing guidance to teams on their product decisions. • In this role, you will be joining the Network Enablement and Access (NEA) team, which is focused on working with our Data Providers to strengthen Plaid’s network and enable great products and experiences. You will collaborate with Product and Engineering to understand Plaid’s network, the current relationships with FIs, and to develop product offerings that will enable a two-way value exchange.

Qualifications

• 5+ years of industry experience in a Product Data Science role • Extensive experience working with funnel conversion and other projects that yielded conversion rate wins • Deep understanding of various statistical techniques and experimentation analysis workflows • Strong familiarity with SQL, data visualization tools, and working knowledge of Python • Prior experience building core data models (preferably using DBT) from scratch • Ability to collaborating closely with Engineers to diagnose and triage data lineage • Data engineering experience and data pipeline tooling (e.g. Airflow, Redshift) experience is a plus • Bachelor's degree or equivalent work experience in Computer Science, Mathematics, Statistics, Operations Research, Economics, or a closely related field

Benefits

• Working closely with product teams to identify important questions and answer them with data. • Communicating analyses and data-backed recommendations to stakeholders • Championing a data-first approach toward decision-making across the entire organization • Defining core data sets and schemas, as well as visualizing and tracking key metrics • Running impactful inferential analyses and data investigations to identify recurring patterns, root causes, and propose actionable product solutions