Machine Learning Engineer

Job Summary

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.

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.