data
Posted Mar 22Data Scientist II - Computer Vision
at Socure
United StatesHybrid
You are nearing today's limit. Upgrade for unlimited access.
Responsibilities
- - Own well-defined components of end-to-end ML pipelines, including data preparation, model training, evaluation, and deployment to production.
- - Collaborate with engineering and product partners to ensure models meet product, performance, and reliability requirements.
Requirements
- experience in computer vision and deep learning to join our document verification team.
- This role is intended for an experienced individual contributor who can work independently on production ML models, own well-scoped modeling initiatives, and contribute to technical decision-making—while partnering closely with senior data scientists and engineers.
- You will help build and improve ML systems that analyze identity and document images at scale and play an active role in evolving our modeling approaches and infrastructure.
- WHAT YOU'LL DO - Develop, maintain, and improve machine learning models for document verification use cases such as document classification, image quality assessment, field extraction, and fraud detection.
- - Write production-quality, maintainable code and contribute to shared ML tooling and infrastructure.
- WHAT YOU BRING - Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field, or equivalent experience, with 5 years or equivalent of professional
- experience in machine learning or data science. MS or Ph.D is a plus. - Strong proficiency in Python and hands-on
- experience with ML frameworks such as PyTorch or TensorFlow. - Solid
- experience applying deep learning models (especially CNNs) in real-world computer vision systems, with working knowledge of transformer-based approaches. - Strong understanding of model evaluation, experimentation, and ML fundamentals, including overfitting, regularization, and transfer learning. -
- Experience with version control (Git), experiment tracking, and reproducible ML workflows. - Ability to communicate technical ideas clearly and work effectively in a cross-functional team.
Benefits
- - Independently implement and evaluate deep learning architectures, including CNNs and transformer-based vision or multimodal models..