Machine Learning Engineer
SweedFull Time
Mid-level (3 to 4 years)
Key technologies and capabilities for this role
Common questions about this position
Yes, this is a fully remote position.
The role uses Python 3, PostgreSQL, and AWS infrastructure including EC2, S3, RDS, and Redshift.
Responsibilities include developing and maintaining fraud detection models through the full lifecycle, researching new fraud types and products, building foundational modeling for fraud and financial risk products, writing production-ready code, and performing analyses for various teams.
SentiLink has a fast-moving environment emphasizing end-to-end ownership, deep domain understanding, critical thinking, and unusual insights over new ML methodologies, with high-visibility projects across teams.
Strong candidates are experienced researchers who are technically capable, can own their domain, excel in full-stack data science including model development and production code, and thrive in high-impact, fast-paced settings with deep fraud domain knowledge.
Machine learning solutions for identity fraud detection
SentiLink provides solutions to help financial institutions prevent identity fraud. Their main product uses machine learning models to detect fraudulent activities during the application process. By analyzing data and reviewing cases with a team of risk analysts, SentiLink offers insights that help clients make informed decisions about approving customers. What sets SentiLink apart from competitors is their ability to adapt their products to various types of fraud and customize them to meet the specific needs of each client. The goal of SentiLink is to enable financial institutions to minimize fraud losses while maintaining a positive customer experience.