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
Our team of trusted audit professionals evaluates every aspect of Fannie Mae's IT environment. From on-premises environments to cutting edge cloud services, our audits cover the broad range of exciting technologies Fannie Mae uses, providing for a challenging environment with tremendous opportunities for personal growth.
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Within IT Audit, the infrastructure team focuses on evaluating Fannie Mae's complex environment of IT processes, systems, and services. We conduct audits focused on highly visible topics, such as cyber security, IT Governance, resiliency, and the management of the various operating systems and platforms used by Fannie Mae.
In this position, you will push us forward in our journey to increase the use of advanced data analytics, modeling, and AI to more effectively assess the IT environment.
THE IMPACT YOU WILL MAKE
The IT Internal Auditor - Lead Associate role will offer you the flexibility to make each day your own, while helping to improve the governance, risk, and control environment related to important risks such as cyber security and resiliency. You will act as a key driver of deploying advanced analytics in our audit work:
- Identify, review, and acquire data from primary or secondary data sources. Establish associated data interfaces and ingestion processing frameworks.
- Implement new statistical modeling capabilities that help identify risks and control gaps in the IT environment.
- Apply and build new advanced analytic capabilities to support the integration of data and statistical models or algorithms into day-to-day IT audit work. Apply industry practices in research and testing to product development, deployment, and maintenance.
- Create new modeling/statistical applications to support risk measurement and automated control testing.
- Design and implement data visualizations, technical documentation, and non-technical presentation materials to communicate complex ideas and findings to audit teams and clients.
- Act as a source of knowledge related to data analytics.
- Build and maintain relationships with business partners.
Qualifications
THE EXPERIENCE YOU BRING TO THE TEAM
Minimum Required Experience
- 4+ years of experience in programming in data analytics related languages, such as Python, R, or JavaScript.
- 2+ years in ML engineering, including 2+ years hands-on with Generative AI/LLMs and 1+ year with knowledge graph technologies.
Desired Experience
- Master's degree in Computer Science, Statistics, Mathematics, or related area of study
- Ability to apply statistical or computational methods to real-world data and tailoring analysis to answer complex questions or problems
- Strong coding skills and experience with data analytics related languages, such as Python (including SciPy, NumPy, and/or PySpark) and/or Scala.
- 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 and experience 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
- Excellent written and verbal communication skills
- Critical thinking and data analytic skills
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.