IT - Internal Audit - Lead Associate - Data Science (Flexible Hybrid)

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.

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

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