Applied AI/ML Senior Associate - Predictive Science

JPMorgan Chase & Co.•Plano, TX

17h

About The Position

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.

Requirements

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

Nice To Haves

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.

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