Applied AI/ML Senior Associate - Predictive Science

As an Applied AI/ML Senior Associate within the Commercial & Investment Bank technology team, you will build and fine-tune language models (including LLMs) that detect and categorize complaints and sentiment across client interactions (calls, chats, emails, surveys). You’ll develop complaint taxonomies and multi-label classifiers, build training/validation/monitoring pipelines, deploy models with MLOps/data engineering partners, and ensure solutions meet privacy, regulatory, and model-risk standards.

Job responsibilities

• Build and fine-tune ML/NLP models (including LLMs) to detect and categorize complaints and sentiment within client interactions (calls, chats, emails, surveys) and develop taxonomies and multi-label classification systems for complaint types, severity, and root cause
• Evaluate, fine-tune, and deploy pre-trained language models; conduct prompt engineering and model evaluation as needed along with build data pipelines for training, validation, and continuous model monitoring
• Analyze model outputs to identify drift, bias, or degradation, and implement retraining strategies
• Ensure models meet regulatory, privacy, and model-risk governance standards
• Collaborate with data engineers and MLOps teams to productionize models at scale
• Present findings and model performance metrics to technical and non-technical stakeholders

Required qualifications, capabilities, and skills

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or related field and 3+ years of experience building and deploying ML/NLP models in production
• Strong Python skills and experience with ML frameworks (PyTorch, TensorFlow, Hugging Face Transformers) with hands-on experience fine-tuning or working with large language models
• Experience with text classification, NER, sentiment analysis, or topic modeling
• Familiarity with cloud ML platforms (AWS SageMaker, Azure ML, or similar)
• Solid understanding of the ML lifecycle: data prep, training, evaluation, deployment, monitoring
• Strong communication skills and ability to work cross-functionally

Preferred qualifications, capabilities, and skills

• Familiarity with model risk management and regulatory requirements
• Experience with vector databases, retrieval-augmented generation (RAG), or LLM fine-tuning techniques (LoRA, PEFT)
• Knowledge of MLOps tools (MLflow, Kubeflow, Airflow)
• Exposure to text classification, NER, sentiment analysis, and cloud ML platforms preferred.