Lead Data Scientist, Machine Learning, Cybersecurity

Wells Fargo - Charlotte, NC

Full-time

Onsite - Charlotte, NC

Credit Intermediation and Related Activities

About the position

Wells Fargo is seeking a Lead Data Scientist specializing in Machine Learning for Cyber Security. The role focuses on building advanced models to detect cyber security anomalies, reduce false positives, and enhance the overall security posture of the organization. The individual will collaborate with cross-functional teams to strategize and execute machine learning initiatives, design and deploy cloud-native platforms, and utilize data science skills to investigate and prevent cyber security threats.

Responsibilities

• Work with cross functional teams to identify, strategize, and execute Machine Learning initiatives focused on Cyber Security.
• Design, code, train, test, deploy, and iterate on large scale cloud-native machine learning platforms for cyber security use cases.
• Investigate data and develop models and analytics to detect and prevent cyber security driven anomalous behaviors.
• Use Link Analysis and Graph based analytics to discover account abuse and identify fraudulent activities.
• Shape the direction of machine learning and artificial intelligence in Cyber Security at Wells Fargo.
• Make decisions on key issues that may arise during development or implementation.
• Collaborate and consult with peers, colleagues, and managers to resolve and achieve goals.

Requirements

• 5+ years of Specialty Software Engineering experience or equivalent through work experience, training, military experience, or education.
• 2+ years of experience working as a Data Scientist.

Nice-to-haves

• 2+ years of experience with Machine Learning (ML), Natural Language Processing (NLP), Large Language Models (LLM), and/or Neural Networks.
• 2+ years of experience with the Machine Learning Development Life Cycle (MDLC).
• 2+ years of experience working with Jupyter notebook, Python, and/or PySpark.
• Experience or exposure in Cybersecurity is highly desirable.
• Exposure to cloud technologies/platforms and cloud-based machine learning certifications are desirable.
• Evidence of real-life experience with end-to-end machine learning projects.
• In-depth knowledge of statistics and machine learning (supervised and unsupervised learning).
• Understanding of current challenges surrounding artificial intelligence: A.I. ethics, fairness, data leakage, model drift, etc.
• Understanding of Learning subfields: Reinforcement learning, Multi-task learning, Transfer learning, and Semi-supervised learning.
• Strong grasp of distributed systems and cloud technologies (Azure, GCP, AWS, etc.).
• Understanding of relational databases.
• Knowledge of data streaming and messaging frameworks (Kafka, Spark Structured Streaming, Flink, etc.).
• SQL experience (any dialect).
• Experience with containers and container-based deployment environment (Docker, Kubernetes, etc.).