Data Scientist II - Computer Vision

About the Role

We are seeking a Data Scientist II with strong 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.

• Develop, maintain, and improve machine learning models for document verification use cases such as document classification, image quality assessment, field extraction, and fraud detection.
• Independently implement and evaluate deep learning architectures
  , including CNNs and transformer-based vision or multimodal models
  .
• Own well-defined components of end-to-end ML pipelines, including data preparation
  , model training
  , evaluation
  , and deployment to production
  .
• Perform in-depth error analysis
  , model diagnostics
  , and performance optimization
  , and propose data- or model-driven improvements.
• Contribute to technical design discussions, code reviews, and modeling best practices across the team.
• Write production-quality, maintainable code and contribute to shared ML tooling and infrastructure.
• Collaborate with engineering and product partners to ensure models meet product, performance, and reliability requirements.

• 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 Py Torch or Tensor Flow
  .
• 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.