🛠 Tech Stack

💼 About This Role

You'll join Socure, the identity trust infrastructure for the digital economy, to design scalable data pipelines and build models that fight fraud and verify identity. You'll own high-impact projects end to end, leveraging agentic AI and LLM-powered systems to automate analysis and accelerate insight generation. This role sits at the intersection of data engineering, machine learning, and fraud detection, working with diverse data types to directly improve products and decisioning.

🎯 What You'll Do

• Design and maintain scalable data pipelines and workflows for fraud detection and model development.
• Build agentic AI and LLM-powered systems to automate data exploration and anomaly detection.
• Develop and optimize models using tabular, text, point cloud, and image data.
• Own data quality and integrity, implementing monitoring and validation checks.

📋 Requirements

• Master’s or PhD in a quantitative field or equivalent experience.
• 5+ years of data science or ML experience in high-growth tech or fintech.
• Experience in fraud prevention, risk modeling, or identity verification.
• Strong proficiency in Python and SQL with ML frameworks (PyTorch, TensorFlow, scikit-learn).

✨ Nice to Have

• Experience with third-party data vendor evaluation and integration.
• Experience with LLMs and agentic AI frameworks.
• Experience with diverse data modalities (tab, text, point clouds, images).

🎁 Benefits & Perks

• 💰 Competitive salary $170K-$200K + equity + bonus
• 🏠 Remote-first culture with flexible work environment
• 📈 Equity grants for all employees
• 🎓 Continuous learning opportunities and mentorship
• 🏖️ Unlimited PTO (implied by startup culture)

📨 Hiring Process

Estimated timeline: 2-3 weeks · AI estimate

1. 1Recruiter Screen· 30 min
2. 2Technical Interview· 60 min
3. 3Hiring Manager Interview· 45 min

🚩 Heads Up

• Job description is extremely long and repetitive, mixing many responsibilities into one role.
• Lists multiple leadership competencies (13+) as expectations, which may indicate unrealistic cultural demands.