Staff Data Scientist (AI/Fraud Detection)

Location: Hybrid, based at the New York or San Francisco hub

Salary: $191,000–$230,000 annual base salary, plus equity, benefits, and an annual bonus or commission plan

Company

hirify.global builds identity trust infrastructure for the digital economy, verifying identities in real time and preventing fraud for businesses, governments, and consumers.

What you will do

• Design, develop, and optimize deep learning models, including transformers, CNNs/RNNs, and graph learning algorithms.
• Build models using tabular data, natural language, point clouds, and images for fraud detection, risk management, and identity verification.
• Lead the full machine learning lifecycle from data exploration and feature engineering through deployment and production monitoring.
• Research new data sources and novel algorithms to advance fraud detection capabilities.
• Collaborate with Product, Engineering, and Risk teams to define data requirements and guide strategic decisions.
• Present technical findings to executive and technical stakeholders while mentoring data scientists and driving project outcomes.

Requirements

• Master’s or PhD in Computer Science, Statistics, Applied Mathematics, Data Science, or a related field, or equivalent professional experience.
• 8+ years of experience in data science, machine learning, or a related field.
• Experience in fraud prevention, risk modeling, or identity verification.
• Hands-on experience developing and deploying deep learning models and working with diverse data modalities.
• Strong proficiency in Python, SQL, and machine learning frameworks such as PyTorch, TensorFlow, and scikit-learn.
• Experience with model deployment, production monitoring, machine learning algorithms, evaluation techniques, and data pipeline development.

Nice to have

• Experience with real-time model inferencing.
• Experience with LLMs and agentic AI frameworks or infrastructure such as LangChain, LangGraph, or Ray.

Culture & Benefits

• Equity and an annual bonus or commission plan in addition to base compensation.
• Work alongside Product, Engineering, and Risk teams on high-impact fraud and identity solutions.
• Emphasis on continuous learning, experimentation, accountability, effective communication, and team development.
• Commitment to diversity, professional integrity, and high standards of business ethics.