Director of Data Science & Artificial Intelligence (AI)

At Fannie Mae, the inspiring work we do helps make a home a possibility for millions of homeowners and renters. Every day offers compelling opportunities to impact the future of the housing industry while being part of a collaborative team thriving in an energizing environment. Here, you will grow your career and help create access to affordable housing finance.

Job Description

In this compelling leadership position, you will plan and direct business unit operations and the work of a team who produce insights, new product or change recommendations, process improvement or automation, and predictive modeling to drive the success of strategy implementation. You will ensure team members are knowledgeable in data mining and data analysis methods, adept with large data processing techniques and computational programing capabilities, have practical problem-solving skills, and possess the ability to articulate solutions to non-technical consumers or partners. As the director, you will develop partnerships across the data engineering and data management teams, and external or created data sources to apply data mining techniques in preparation for analysis or use of enterprise data assets.

• Lead a team of data scientist and AI developers, inspire innovation and development of advanced AI solutions from inception to production.
• Drive advancements in AI, while shaping the future of AI in mortgage industry and supporting the company's mission.
• Ensure collaboration with product and/or business owners, data engineers, and platform teams to align team objectives and group strategy.
• Oversee the application of AI and data science techniques from disciplines, such as computer science, computational science and methods, statistics, econometrics, data optimization, and data visualization. Ensure statistical modeling capabilities meet the group's strategic needs.
• Direct and execute the deployment of AI capabilities, Generative AI solutions, recommender systems, predictive analytic capabilities to enhance the delivery of business applications and support the integration of data and statistical models or algorithms.
• Apply innovative practices in data science and AI research and testing to product development, deployment, and maintenance.
• Direct the design of modeling applications to resolve complex or unusual business problems.
• Ensure the team communicates complex ideas and solutions effectively to division leadership through data visualizations, technical documentation, and non-technical presentation materials.

• 8 years of relevant experience in AI, data science, or related fields, with a proven track delivering solutions to production
• Exceptional leadership skills, with experience in building, mentoring, and guiding high-performing, diverse teams of data scientists and AI professionals.
• Exemplary communication and stakeholder management skills, adept at engaging with leadership and key stakeholders to drive consensus and action.
• A spirit of scientific discovery, driven by a passion for innovation to deliver results, balanced with a deep understanding of risks and ethical considerations.
• Strong proficiency in programming languages such as Python, R, and SQL, crucial for data manipulation and algorithm development.
• In-depth knowledge of cloud computing environments such as AWS, Azure, or Google Cloud Platform, particularly their AI and data analytics services.
• Bachelor's degree in computer science, Math, Statistics, engineering, physics or related field or equivalent experience

• Master degree or PhD in computer science, Math, Statistics, engineering, physics or related field is preferred
• Demonstrated success in developing and deploying AI-driven solutions and models, particularly within the Financial or professional services sectors.
• Profound understanding of AI and advanced analytics technologies, coupled with the ability to evaluate their feasibility.
• Ideally, 10+ years of experience in Machine Learning, delivering complex prototyping solutions to production.
• Extensive proven, hands-on experience in data science. Expert-level experience with Natural Language Processing (NLP), Natural Language Understanding (NLU) and Natural Language Generation (NLG).
• Extensive experience with advanced data analysis and statistical methods such as regression, hypothesis testing, ANOVA, time-series analysis, statistical process control, are preferred
• Practical applications of machine learning techniques such as Clustering, Logistic Regression, CART, Random Forests, SVM or Neural Networks.
• Expert-level knowledge of deep learning frameworks such as TensorFlow, PyTorch, and other open sources libraries / APIs or similar. Strong technical and problem-solving skills and evidence of continuous learning in the analytics field
• Breadth and depth of knowledge in the application of statistical and/or digital methods to solve business problems
• Proficiency with Python and basic libraries for machine learning. Ability to visualize & synthesize results. Full stack experience building GenAI solutions; Large language models, language transformers (BERT, RoBERTa) data prep & vectorization, embedding/chunking, prompting, search/summary/RAG/finetuning.
• Experience with deep learning (e.g., CNN, RNN, LSTM) methods.
• Experience building NLP and NLG tools and a wide range of LLMs (Llama, Claude, OpenAI, etc.), LoRA, LangChain, RAG, LLM Fine Tuning and PEFT are preferred.
• Demonstrated skills with Jupyter Notebook, AWS Sagemaker, or Domino Datalab or comparable environments
• Passion for solving complex data problems and generating cross-functional solutions in a fast-paced environment

• Proficient in big data technologies such as Hadoop, Spark, and Kafka for handling large datasets.
• Proficient in using AI/ML platforms like Google AI Platform, AWS SageMaker, or Azure Machine Learning for model development and deployment.
• Expertise in popular machine learning algorithms and libraries such as TensorFlow, PyTorch, and Keras.
• Experience with data visualization tools like Tableau, Power BI, or Qlik for deriving actionable insights from data.
• Strong proficiency in programming languages such as Python, R, and SQL, crucial for data manipulation and algorithm development.
• In-depth knowledge of cloud computing environments such as AWS, Azure, or Google Cloud Platform, particularly their AI and data analytics services.
• Experience with database management and querying tools, including traditional SQL databases (e.g., MySQL, PostgreSQL) and NoSQL databases (e.g., MongoDB, Elastic Search).
• Familiarity with Amazon Bedrock, AmazonQ, or Google Vertex or Microsoft AI services is prefered
• Familiarity with DevOps practices and tools (e.g., Jenkins, Docker, Kubernetes) for efficient deployment of AI solutions.
• Understanding of MLOps principles to streamline the machine learning lifecycle from experimentation to production.
• Knowledge of security protocols and compliance standards relevant to data privacy and AI