Lead Machine Learning Scientist
Fraud
Confirmed live in the last 24 hours
Varo

501-1,000 employees

Digital, FDIC-insured bank offering high-yield savings and quick money
Company Overview
Varo Bank, an all-digital, FDIC-insured institution, merges the expertise of banking veterans with tech specialists to provide premium banking services, including high-yield savings, automatic saving tools, and early access to paychecks. The company's commitment to customer-centric innovation is evident in its unique offerings such as Varo Advance, providing quick access to cash, and Varo Believe, a program designed to help customers build credit. With one of the highest savings rates in the country and a strong emphasis on a fee-free banking experience, Varo has successfully positioned itself as a leader in the digital banking industry.
Fintech

Company Stage

Series E

Total Funding

$996.3M

Founded

2015

Headquarters

San Francisco, California

Growth & Insights
Headcount

6 month growth

-2%

1 year growth

-2%

2 year growth

-2%
Locations
Remote in USA
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Apache Spark
Keras
Pytorch
Tensorflow
CategoriesNew
AI & Machine Learning
Requirements
  • 5+ years of expertise in developing ML industry applications for fraud or risk applications
  • Demonstrated experience and/or interest in building fraud detection machine learning models
  • Ph.D. or equivalent in Computer Science, Statistics, or related field
  • Strong bias for action and team player
  • Ability to thrive in a fast-paced environment
  • Preferred: Familiarity with Sagemaker, Spark, TensorFlow, Keras, and PyTorch
Responsibilities
  • Lead efforts within the organization to drive the design, development, optimization, and productionization of ML-based fraud models
  • Enable customers to have broader access to risky product features by designing a fraud system that balances precision of detection with customer experience and growth
  • Work with stakeholders in the fraud organization to identify opportunities for driving business value with ML
  • Collaborate cross-functionally with the engineering team to deploy models and monitor outcomes
  • Guide and mentor junior machine learning scientists and engineers