Senior Data Scientist - AI Developer (Flexible Hybrid)

Target Hiring Range (1): 121000

Target Hiring Range (2): 158000

Company Description

At Fannie Mae, futures are made. The inspiring work we do helps make a home a possibility for millions of homeowners and renters. Every day offers compelling opportunities to use tech to tackle housing's biggest challenges and impact the future of the industry. You'll be a part of an expert team thriving in an energizing, flexible environment. Here, you will grow your career and help create access to fair, affordable housing finance.

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 with LLM 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 & 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 with MLOps tools (Docker, Kubernetes, MLflow).
  • Knowledge & 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

Additional Information

The future is what you make it to be. Discover compelling opportunities at careers.fanniemae.com.

Fannie Mae is a flexible hybrid company. We embrace flexibility for our employees to work where they choose, while also providing office space for in-person work if desired. At times, business need may call for on-site collaboration, which means proximity within a reasonable commute to your designated office location is preferred unless job is noted as open to remote.

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 [email protected].

The hiring range for this role is set forth on each of our job postings located on Fannie Mae's Career Site. Final salaries will generally vary within that range based on factors that include but are not limited to, skill set, depth of experience, certifications, and other relevant qualifications. 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. See more here.