Responsibilities

Collaborate with global teams to deliver high-impact data products for worldwide deployment.
Develop and maintain ML pipelines to optimize critical processes for global lending products, including anti-fraud systems, credit strategy, and marketing optimization.
Enhance and maintain scalable machine learning infrastructure to support robust data product performance.
Mentor engineers and data scientists on best practices in ML engineering, fostering a culture of excellence and knowledge sharing.
Lead data product development from ideation through to large-scale deployment, ensuring end-to-end execution.

Qualifications

PhD or Master’s degree in Computer Science, Statistics, Engineering, or a related field.
5+ years of experience as a Machine Learning Engineer, Data Scientist, or in a closely related role, with a proven track record of delivering impactful solutions.
Deep expertise in machine learning algorithms, frameworks, and the full ML lifecycle, including data extraction, feature engineering, model serving, and monitoring for both live and batch processing.
Strong software design skills with high proficiency in Python and related libraries/frameworks (e.g., Scikit-Learn, Pandas, Flask, FastAPI).
Excellent interpersonal skills, with strong written and verbal communication abilities.
Demonstrated experience with cloud providers (AWS preferred) and associated data services.