Machine Learning Engineer

Job Summary

Clear Grid is seeking an innovative and hands‑on ML Data Scientist to join our Collections and Portfolio Data Science team. In this role, you will own the end‑to‑end lifecycle of cutting‑edge AI/ML models designed to optimize recovery efforts for unsecured credit products (BNPL, installment loans, etc.). You will act as a technical leader, driving data strategy and deploying Agentic AI solutions that directly impact hundreds of thousands of customers while ensuring model fairness and regulatory compliance.

• Design, build, and deploy ML models to predict default risk and recovery probabilities for short‑term lending products.
• Take complete hands‑on ownership of the model lifecycle, from initial research to production monitoring and automated retraining.
• Implement creative statistical approaches, including causal analysis, reinforcement learning, and natural language processing.

• Build efficient, reusable data pipelines for feature generation and model scoring using Python and SQL.
• Architect and deploy Agentic AI infrastructures, utilizing both proprietary and commercially available tools.
• Collaborate with engineering teams to integrate models into production environments seamlessly.

• Develop models for call center performance, utilizing causal analysis and multi‑armed bandits to optimize outreach.
• Manage collection modeling concepts such as PD calibration, reject inference, and risk segmentation.
• Partner with agency and portfolio purchase teams to align model outputs with actionable business lending decisions.

• Ensure all models meet strict standards for fairness, interpretability, and adverse action logic.
• Contribute to the continuous evolution of Clear Grid’s ML infrastructure to improve the efficiency of our AI ecosystem.

• Advanced Degree (Ph.D. / MS) in Computer Science, Data Science, AI, Mathematics, or a related quantitative field.
• 3–5 years of professional experience in AI Science or Machine Learning.
• Fintech Expertise:
  Significant experience in credit risk, with a deep understanding of payment systems, banking, and lending products.

• Programming:
  Authoritative knowledge of Python and SQL.
• Frameworks:
  Proficiency in Tensor Flow, PyTorch, and tree‑based models.
• ML Techniques:
  Expertise in deep learning, clustering, time series, and reinforcement learning.
• Data Engineering:
  Proven ability to build scalable pipelines and frameworks for large, complex datasets.

• Strong problem‑solving abilities and the communication skills necessary to defend model logic to stakeholders.
• A results‑oriented “innovator” mindset, capable of thriving in a fast‑paced, collaborative environment.

• Cloud Infrastructure:
  Experience with GCP (Vertex AI) or AWS (Sage Maker) and orchestration tools like Apache Airflow.
• MLOps:
  Strong background in automated retraining, version control, and model monitoring pipelines.
• Experimentation:
  Expertise in A/B testing design and advanced statistical analysis.