Lead Associate — Generative AI & Applied Data Science

Fannie Mae•Washington, DC

5d•$141,000 - $184,000•Hybrid

About The Position

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.

Requirements

4 years of experience.
Bachelor’s degree in Computer Science, Data Science, Engineering, Finance, Mathematics, Physics, Statistics, Business Analytics, or a related field.
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.

Nice To Haves

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).
NLP toolkits and techniques: spaCy, NLTK, topic modeling, sentiment analysis, NER, POS tagging, TF-IDF, text embedding workflows.
Familiarity with LLM-centric architectures and patterns: agentic programming / LLM Agents, Chain-of-Thought, Tree-of-Thought, Human-in-the-Loop (HITL) design.
Experience with search and indexing technologies (Elasticsearch / OpenSearch / Solr) and knowledge graphs/ontologies (OWL, RDF, SPARQL) is a plus.
Image model knowledge (e.g., ResNet, CLIP) is a plus for multimodal use cases.
Experience building reproducible, productionized data workflows and visualizations (Tableau, Kibana, QuickSight, etc.).
Demonstrated success translating analytics into business impact in a fast-paced, cross-functional environment.
Strong scripting skills (Shell, Python) and familiarity with Spark / PySpark for large-scale data processing.
Comfortable working with ambiguous problems, imperfect data, and evolving requirements.

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.

Benefits

This position is eligible to participate in a Fannie Mae incentive program (subject to the terms of the program).
As part of our comprehensive benefits package, Fannie Mae offers a broad range of Health, Life, Voluntary Lifestyle, and other benefits and perks that enhance an employee's physical, mental, emotional, and financial well-being.

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