Machine Learning Scientist (Financial Scoring)
TLDR
Develop innovative machine learning models to provide underserved borrowers with better access to credit and improve model performance through collaboration and rigorous testing.
Build machine learning and deep learning models based on financial and other modalities like images and text
Collaborate closely with cross-functional teams to integrate machine learning models seamlessly into production systems
Conduct extensive analysis to better understand our continually evolving datasets and validate model performance
Design and develop robust, scalable pipelines and services using software engineering best practices
Implement end-to-end solutions, including architecture design, business logic, and deployment
Drive the adoption of rigorous testing practices, ensuring high-quality, reliable ML model releases
Stay updated with advancements in machine learning and related technologies to continuously improve our solutions
Ph.D in Computer Science, Statistics, Mathematics or a related field
2+ years of relevant industry or postdoc experience
Solid understanding of computer science fundamentals, including data structures and algorithms and object oriented programming
Deep understanding of machine learning models and algorithms
Deep understanding of probability and statistics
Strong data analysis skills and experience working with messy real world data
Proficiency in python and familiarity with commonly used machine learning libraries such as numpy, pandas, torch, and scikit-learn
Excellent communication skills
Background Checks
We’ve been made aware of individuals falsely claiming to represent Lendbuzz using lookalike email addresses (eg @lendbuzzcareers.com). Please note that all legitimate emails from our team come from @lendbuzz.com. We will never ask for sensitive information or conduct interviews via messaging apps.
Lendbuzz is a fintech company that focuses on transforming the automotive lending landscape by providing underserved borrowers with personalized and equitable access to credit. Our innovative technologies aim to empower both borrowers and automotive dealers, fostering a culture of diversity and success in the industry.
- Founded
- Founded 2015
- Employees
- 11-50 employees
- Industry
- Internet Software & Services
- Total raised
- $180M raised