Data Scientist II (AI)

Location: Hybrid, with hubs in San Francisco, Miami, New York, and Seattle

Salary: $140K–$170K base salary annually, plus equity, benefits, and an annual bonus or commission plan.

Company

Builds identity trust infrastructure for the digital economy, focusing on real-time identity verification and fraud prevention.

What you will do

• Design, develop, and implement deep learning models including transformers, CNNs, and LSTMs for fraud and risk challenges.
• Build and optimize models using tabular data, text, point clouds, and images.
• Own assigned technical and functional work with minimal supervision.
• Contribute across the machine learning lifecycle, including data exploration, feature engineering, training, evaluation, deployment, and monitoring.
• Collaborate across teams and communicate technical results to technical and non-technical audiences.
• Apply current AI and machine learning advances to fraud prevention, risk management, and identity verification.

Requirements

• Bachelor’s degree with substantial related experience, a relevant master’s degree, or equivalent experience in computer science, statistics, mathematics, engineering, or a related field.
• 2–4 years of hands-on experience developing and deploying deep learning models.
• Experience with diverse data modalities, including tabular data, text or language, point clouds, and images.
• Proficiency in Python and major machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn.
• Understanding of machine learning algorithms, model evaluation, data pipelines, and production deployment and monitoring.
• Strong problem-solving, independent working, collaboration, and communication skills.

Nice to have

• Experience with real-time model inference in production.
• Experience with LLMs and agentic AI frameworks or infrastructure such as LangChain, LangGraph, or Ray.
• Experience in fraud prevention, risk modeling, or identity verification.

Culture & Benefits

• Mission-driven work in identity verification and fraud prevention.
• Challenging projects using advanced technology.
• Inclusive and collaborative environment with opportunities for professional growth.
• Competitive compensation, equity, and benefits.