Machine Learning Engineer

Job Summary

ClearGrid 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.

Key Responsibilities

Model Development \& Lifecycle Ownership:

• 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.

Data Strategy \& Engineering:

• 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.

Domain-Specific Optimization:

• 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.

Compliance \& Infrastructure Evolution:

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

Requirements

Educational \& Professional Experience:

• 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.

Technical Skills:

• Programming: Authoritative knowledge of Python and SQL.
• Frameworks: Proficiency in TensorFlow, 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.

Soft Skills:

• 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.

Preferred Qualifications

• Cloud Infrastructure: Experience with GCP (Vertex AI) or AWS (SageMaker) 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.