Senior AI Scientist (Fraud Detection)

Location: San Diego, California, United States

Salary: $165,500–$223,500 base pay annually, with potential bonus, equity rewards, and benefits.

Company

hirify.global is a financial technology platform serving customers through products including TurboTax, Credit Karma, QuickBooks, and Mailchimp.

What you will do

• Design, build, deploy, monitor, retrain, and improve fraud and abuse detection models across account creation, authentication, and in-product activity.
• Discover data sources, build ETL pipelines, engineer features, and develop models for real-time and batch risk scoring.
• Develop entity-level anomaly detection, graph and link analysis, clustering, and behavioral modeling approaches for emerging attack patterns.
• Apply generative and agentic AI to unstructured signals, model explanations, and investigation workflow automation.
• Design experiments and champion/challenger tests to establish operating thresholds and downstream actions.
• Partner with Policy, Investigations, AI Engineering, ML Engineering, and Analytics to turn detection signals into enforcement actions and business outcomes.

Requirements

• MS or PhD in Computer Science, Statistics, Applied Mathematics, Operations Research, Physics, or a related quantitative discipline.
• 4+ years of industry experience building and deploying production machine learning models.
• Expert proficiency in Python and SQL, with experience in scikit-learn, gradient boosting, PyTorch or TensorFlow, pandas, and NumPy.
• Strong knowledge of statistical modeling, classification, regression, clustering, anomaly detection, neural networks, and tree ensembles.
• Experience with class imbalance, delayed or noisy labels, precision-recall trade-offs, threshold selection, and cost-sensitive metrics.
• Proficiency with Spark or equivalent large-scale data ecosystems and comfort working in Linux.

Nice to have

• Experience in fraud, risk, abuse, security, anti-money-laundering, or another adversarial modeling domain.
• Experience with graph-based entity resolution, ring detection, graph neural networks, or community detection.
• Experience with real-time model serving, feature stores, model monitoring, MLOps, or frequent retraining.
• Production experience applying LLMs to unstructured text classification or agentic workflows.
• Experience working with investigations, operations, or policy teams where model outputs drive human review and enforcement.

Culture & Benefits

• Work with a distributed AI Science organization within Trust & Safety.
• Collaborate across Policy, Investigations, ML Engineering, Analytics, product, engineering, and compliance functions.
• Competitive compensation with performance-based rewards.
• Potential eligibility for cash bonus, equity rewards, and employee benefits.