Job Description Fannie Mae is expanding its Data Science talent to further push the frontiers of modeling, AI and advanced analytics. Are you passionate about advanced analytics algorithms, AI techniques and about creating new AI solutions and technologies? Do you have creative and innovative approaches to developing new AI products? We’re seeking data scientists who have domain knowledge or an interest in Generative AI, large language models, machine learning, natural language processing, image processing and an interest to apply it to solve the most complex problems in business.

If you are ready for an exciting opportunity working hands on with the world’s most advanced data science technologies and thrive in a super dynamic environment where you are being counted on to develop advanced analytics and AI products, this role is for you:

THE IMPACT YOU WILL MAKE

The Senior Data Scientist – AI Developer 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:

• Collaborate with product and/or business owners, data engineers, and platform teams to understand business needs and current capabilities, data availability, and alternative uses.

• Implement new statistical modeling capabilities.

• Apply analytic capabilities and build upon advanced analytic capabilities to enhance the delivery of business applications, and support the integration of data and statistical models or algorithms. Apply industry 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 complex ideas and solutions to business partners.

Qualifications THE EXPERIENCE YOU BRING TO THE TEAM

Minimum Required Experiences:

• Education: Bachelors degree in Computer Science, Data Science, Statistics, Physics, Mathematics, or related quantitative field.

• Experience: 2+ years in ML engineering, including 2+ years hands-on with Generative AI/LLMs and 1+ year with knowledge graph technologies.

• Technical Expertise:

• Generative AI:

• Proven experience building AI solutions using advanced prompt engineering (Chain of Thought, Tree of Thought) and designing and deploying RAG pipelines

• Experience with validation of LLM outputs and reduction of hallucinations

• Knowledge of Agentic AI architecture, and knowledge graph integration with LLMs (e.g., GraphRAG, ontology-driven prompt engineering, hybrid reasoning systems).

• Hands-on work with vector databases (Pinecone, Chromadb) and frameworks like LangChain/LlamaIndex for orchestration.

• Classical Machine Learning:

• Strong foundation in supervised/unsupervised learning (regression, classification, clustering, ensemble methods).

• Experience combining classical ML (e.g., feature engineering, dimensionality reduction) with GenAI systems for improved robustness/accuracy.

• Proficient in Natural language processing (NLP) and Natural language generation (NLG)

• Tools:

• Proficient in Python, PyTorch/TensorFlow, and ML libraries (Scikit-learn, Hugging Face Transformers).

• Production experience with AWS/GCP (SageMaker, S3, Lambda)

• Demonstrated experience building data pipeline to process structured and unstructured data sources, data cleansing/prep for analysis

• Demonstrated experience with code repositories and build/deployment pipelines, specifically Jenkins and/or Git/GitHub/GitLab

Desired Experiences:

• Education: MS/PhD in Computer Science, Data Science, Statistics, Physics, Mathematics, or related quantitative field.

• GenAI Experience

• Experience withLLM fine-tuning (LoRA, PEFT), and multi-agent systems (e.g., AutoGen, CrewAI).

• Experience with ontology design for domain-specific GenAI applications (e.g., finance, healthcare).

• Knowledge Graph and ; GenAI Synergy:

• Building dynamic knowledge graphs from unstructured data (e.g., LLM-generated content) and using them for retrieval/validation.

• Experience with ontology design for domain-specific GenAI applications (e.g., finance, healthcare).

• Classical ML + GenAI Hybridization:

• Using classical ML for bias detection, anomaly monitoring, or performance optimization in GenAI workflows.

• Experience with image processing models such as Coco, CLIP, ResNet or comparable models

• Hybrid modeling (e.g., combining classical ML and GenAI)

• Advanced Tools:

• Graph ML: NetworkX, PyTorch, Graph Neural Network (GNN).

• Experience withMLOps tools (Docker, Kubernetes, MLflow).

• Knowledge and ; experience with microservices, service mesh, API development and test automation

• Data Engineering:

• Experience with graph databases (Neo4j, AWS Neptune)

• Experience with Search/Retrieval: ElasticSearch, AWS OpenSearch, or semantic search architectures.

• Research Mindset:

• Publications or open-source contributions in AI/ML (e.g., knowledge graph-enhanced LLMs, causal ML).

Skills

• Strong customer-centric problem-solving mindset

• Ability to translate business ideas into analytics models that have major business impact

• Demonstrated experience working with multiple stakeholders

• Demonstrated communication skills, e.g. explaining complex technical issues to more junior data scientists, in graphical, verbal, or written formats.

• Comfortable working with ambiguity (e.g. imperfect data, loosely defined concepts, ideas, or goals)

• Demonstrated experience developing tested, reusable and reproducible work.

• Transparently documenting code and methodologies

Fannie Mae is an Equal Opportunity Employer, which means we are committed to fostering a diverse and inclusive workplace. All qualified applicants will receive consideration for employment without regard to race, religion, national origin, gender, gender identity, sexual orientation, personal appearance, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation in the application process, email us at careers_mailbox@fanniemae.com.