Lead Associate — Generative AI & Applied Data Science

Playing an essential role in the U.S. economy, Fannie Mae is foundational to housing finance. Here, your expertise can help fuel purpose-driven innovation that expands access to homeownership and affordable rental housing across the country. Join Fannie Mae to grow your career and help people find a place to call home.

Job Description

We’re hiring a Lead Associate to join our Applied Data Science team to build and productize advanced AI/ML and generative AI solutions that drive business outcomes. The role partners closely with cross-functional stakeholders (business, risk, controls, and engineering) to deliver production-grade models, scalable data products, and explainable AI that operate in a regulated financial environment.

We are especially interested in candidates who have experience at a large financial institution, are familiar with financial datasets and workflows, and have hands-on experience building GenAI / LLM solutions. A PhD in finance, economics, computer science (or strongly related field) is preferred.
 

THE IMPACT YOU WILL MAKE

The Lead Associate — Generative AI & Applied Data Science role will offer you the flexibility to make each day your own, while working alongside people who care so that you can deliver on the following responsibilities:
 

• Coordinate product and/or business owners across divisions or product lines, data engineers, and platform teams to define business needs and advise on current capabilities, data availability, and alternative uses.
• Lead the implementation of new statistical modeling capabilities which requires skillful coordination of multiple processes, systems, and/or stakeholders.
• Apply advanced analytic capabilities to enhance the delivery of business applications, and support the integration of data and statistical models or algorithms.
• Apply new or innovative practices in research and testing to product development, deployment, and maintenance.
• Design new modeling applications to support risk measurement, financial valuation, decision making, and business performance.
• Design data visualizations, technical documentation, and non-technical presentation materials to communicate new ideas and high-impact solutions to business partners.

Minimum Required Experiences:
 

• 4 years of experience.
• Bachelor’s degree in Computer Science, Data Science, Engineering, Finance, Mathematics, Physics, Statistics, Business Analytics, or a related field. PhD preferred (see desired).
• Experience working at a large financial institution and demonstrable familiarity with financial accounting, capital, or mortgage/loan data and workflows.
• 2+ years of relevant industry experience building large-scale machine learning or deep learning models/systems (for Lead Associate level, 3+ years is preferred).
• Hands-on programming experience in Python (3+ years recommended) and familiarity with Linux-based environments.
• Experience working in cloud environments (e.g., AWS) and comfortable with tools such as SageMaker, Jupyter, Spark.
• Practical experience with NLP, NLG and Large Language Models (LLMs) and GenAI tools (for example: GPT-4, OpenAI APIs, LLaMA, Claude, etc.).
• Demonstrated experience with model development and MLOps workflows (data prep, training, evaluation, CI/CD, model deployment, monitoring). Familiarity with Git, build/deploy tools (Jenkins/GitHub Actions/GitLab CI), and container workflows (Docker/Kubernetes) is expected.
• SQL skills and experience with relational and analytics databases (e.g., Redshift, Postgres, Oracle, Hive, EMR).
• Excellent written and verbal skills and the ability to proactively communicate and collaborate with stakeholders across business, engineering, and controls teams.

Desired Experiences:
 

• PhD (preferred) or MS in Finance, Economics, Computer Science, Statistics, Math, or a related field.
• 3+ years building large-scale ML/DL systems in production, with at least 1 year of focused deep-learning / LLM/GenAI work.
• Prior experience developing and deploying LLM agents or agentic systems.
• Experience with MLOps platforms (Domino, Sagemaker, or similar) and CI/CD for ML.
• Deep learning frameworks: TensorFlow, Keras, PyTorch.
• Applied NLP/GenAI frameworks: Hugging Face transformers, LangChain, RAG architectures, LoRA, PEFT, LLM fine-tuning approaches.
• Vector search / retrieval: Vector DBs, FAISS, Milvus, Pinecone, or cloud equivalents; experience implementing retrieval-augmented generation (RAG).
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