Review machine learning, AI, and Generative AI models across TD to identify potential fairness and bias risks.
Represent Compliance in major projects, initiatives, and enterprise engagements.
Perform quantitative analyses to identify fair lending risks associated with model development and use.
Identify and recommend alternative approaches to mitigate fair lending bias arising from models.
Compile, analyze, and generate ad-hoc analytical reports for internal stakeholders.
Communicate complex analytical findings clearly to non-technical audiences.

Bachelor's degree required, advanced technical degree preferred (e.g., math, physics, engineering, finance or computer science)
Master's degree in computer science, machine learning, AI, engineering, statistics, data science, mathematics or related field and 2+ years of relevant work experience in either developing or validating Generative AI, LLMs, NLP and other Deep Learning models
In-depth knowledge of AI/ML methodologies, concepts and theory including Generative AI, Deep Learning, modern Natural Language Processing (NLP), Retrieval-Augmented Generation (RAG), Transformers, Diffusion models etc.
Experience with Deep Learning and Generative AI technology stacks and libraries such as PyTorch, PromptFlow, LangChain, HuggingFace, etc.
Proficient in one or more scripting/programming languages such as Python.
Familiarity with cloud platforms (e.g., Azure, AWS) and big data technologies (e.g., PySpark, Hadoop).

Applied Machine Learning Scientist I

Key skills