Data Science -Fraud & Risk

We are seeking a skilled and motivated Data Scientist II to join a Fraud & Risk Data Science team. This is an individual contributor role offering meaningful autonomy and ownership over day‑to‑day execution, with a strong emphasis on delivering high‑impact, production‑ready solutions. You will work hands‑on with advanced machine learning and deep learning models, partnering closely with cross‑functional stakeholders to support fraud detection, risk mitigation, and identity‑related use cases. The role provides exposure to a variety of problem domains within a collaborative, high‑performing team environment.

• Design, develop, and deploy advanced machine learning and deep learning models, including transformers, convolutional neural networks (CNNs), and LSTMs, to solve complex fraud and risk problems.
• Build and optimize models using diverse data modalities such as tabular data, natural language text, images, and other structured and unstructured inputs.
• Own assigned workstreams end‑to‑end, executing technical and analytical tasks with minimal supervision to support project and business objectives.
• Contribute across the full machine learning lifecycle, including data exploration, feature engineering, model training, evaluation, deployment, and monitoring.
• Collaborate effectively with data science, engineering, and product partners, sharing knowledge and incorporating feedback to drive strong outcomes.
• Make sound technical decisions within your scope and proactively contribute to broader team and functional goals.
• Stay current with emerging AI and machine learning techniques and apply them pragmatically to real‑world challenges.
• Communicate technical findings and business insights clearly to both technical and non‑technical audiences.
  
  

• Bachelor's degree with relevant experience, Master's degree, or equivalent practical experience in computer science, statistics, mathematics, engineering, or a related field.
• 2-4 years of hands‑on experience developing and deploying machine learning or deep learning models in production environments.
• Experience working with multiple data types, including structured tabular data, text/NLP, and image‑based data.
• Strong proficiency in Python and familiarity with common ML frameworks and libraries (e.g., PyTorch, TensorFlow, scikit‑learn).
• Solid understanding of core machine learning concepts, model evaluation practices, and data pipeline development.
• Experience deploying, monitoring, and maintaining models in production; real‑time inference experience is a plus.
• Exposure to large language models (LLMs) and agent‑based or orchestration frameworks is a plus.
• Strong problem‑solving skills and the ability to work both independently and collaboratively.
• Proven ability to operate effectively in cross‑functional team settings.
• Clear written and verbal communication skills.
• Prior experience in fraud, risk modeling, or identity‑related domains is a plus.