Data Scientist - Payments

Plaid

🔍 Observed

$162K - $222K USD

San Francisco First seen 3 weeks ago Last seen 1 hour, 12 minutes ago Lever

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Job Description

AI Summary

• Apply quantitative analysis, data mining, and visualization techniques to improve Plaid's API products and conduct large-scale A/B experiments for the payments team • Requires 3+ years of product-focused data science experience with proficiency in SQL, Python, data visualization tools, and understanding of modern ML techniques (classification, clustering, neural networks, NLP) • Build data pipelines and identify signals for machine learning models while informing product and engineering decisions through data analysis and presentations • Influence long-term data science strategy at Plaid and champion a data-first decision-making approach across the organization • Preferred qualifications include experience with AWS stack and data pipeline tools like DBT or Airflow; degree in Computer Science, Statistics, Engineering, Economics, or related field required

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, Washington D.C., London and Amsterdam.

Plaid’s Data team is building data insights and models that improve how millions of users understand and grow their financial lives. We're looking for data scientists with experience applying state-of-the-art data analytics techniques to different product areas. We value not only technical know-how, but also creativity, user empathy, and teamwork.

You’ll be a data scientist as a part of the Data organization, contributing to diverse, high-impact data insights and machine learning challenges. Specifically, you will focus on driving business impact, and production directions through data analysis and potentially help evaluate and improve ML solutions and systems. If you're interested in building state-of-the-art Data/ML solutions to unblock financial freedom for everyone, let's chat!

Responsibilities: Applying your expertise in quantitative analysis, data mining, and data visualization to find insights for improving our API products Conducting large-scale A/B experiments Identifying useful signals that can be fed into machine learning models. Informing and influencing product and engineering teams through your data analysis and presentations Making long-term data science roadmap decisions like how machine learning and data science iteration should be done at Plaid Championing a data-first approach toward decision-making across the entire organization. Qualifications: 3+ years of industry experience in a product-focused Data Science role Deep familiarity with SQL and data visualization tools Experiencing conducting large-scale A/B experiments, analyzing results, and translating them into concrete recommendations Familiarity with the AWS stack Understanding of modern machine learning (ML) techniques, such as classification, clustering, optimization, deep neural network, and natural language processing Proven ability to tailor your solutions to business problems in a cross-functional team Ability to code and iterate independently in Python to conduct exploratory data analysis Experience building data pipelines in DBT or Airflow is a plus Bachelor's degree or equivalent work experience in Computer Science, Statistics, Engineering, Economics, or a closely related field

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Job Information

Company:
Plaid
Location:
San Francisco
Experience Level:
Senior
Source:
Lever
Status:
Active

Activity Score

68 /100

Likely Active (68)

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