Lead Data Engineer - Data Scientist

Wells Fargo•Minneapolis, MN

2d•$119,000 - $206,000•Onsite

About The Position

Wells Fargo is seeking a Lead Data Engineer in Cybersecurity as part of Identity Access Management. This role sits at the intersection of cybersecurity, data science, machine learning, and AI. The successful candidate will develop intelligent solutions that improve access governance, identify anomalous behavior, automate entitlement decisions, and generate actionable insights across Wells Fargo's Identity and Access Management ecosystem.

Requirements

5+ years of Database Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
5+ years of experience with Python data science libraries such as Pandas, NumPy, and Scikit-Learn, together with exposure to a deep learning framework such as TensorFlow or PyTorch

Nice To Haves

Knowledge of Vertex AI, GCP environments, BigQuery and getting models into production on these platforms
Knowledge of graph networks for anomaly detection (Neo4j)
A learning mindset that keeps up-to-date with the latest developments in the field
Knowledge of Github for code management, Power BI, Tableau, Alteryx
Understanding of software engineering fundamentals including testing, debugging, code reviews etc is highly desirable
Knowledge of the mathematical foundations of statistics, machine learning, and modern AI techniques.
Strong understanding of model evaluation techniques, particularly for classification and clustering models including metrics such as precision, accuracy, F1-scores, confusion matrices, ROC curves etc.
High level of comfort with the use of AI coding agents such as Github Copilot, Claude Code, Cursor, Devin or others with the ability to critically examine AI code for fitness for use.
Familiarity with LLMs, RAG, and AI agents
Strong SQL skills and ability to work with relational and analytical databases.
Strong book-of-work management and organizational skills.
Hands-on ability to manipulate data and tools to prototype and present solutions.
Confident and self-motivated producer of original ideas and solutions, with sound judgment for when to escalate issues
Strong written and verbal communicator with excellent presentation skills
Ability to communicate complex technical concepts to colleagues and team members.
Bachelor’s or Master’s degree in Computer Science, Data Science, AI, Engineering, or related field.

Responsibilities

Lead complex initiatives with broad impact and act as key participant in large scale software planning for the Identity and Access management.
Design, develop, and run tooling to discover problems in data and applications and report the issues to engineering and product leadership.
Apply knowledge of statistical and data science methods and techniques to business problems relating to Identity and Access Management.
Serve as a subject matter expert on ML, AI and the application of mathematical and statistical techniques to large datasets.
Design, support and operate data pipelines, data models, dashboards, and API integrations to deliver real-time and batch analytics use cases.
Design and conduct experiments, statistical analyses, and hypothesis testing to evaluate proposals in support of controls, policies, and operational processes.
Build AI-powered capabilities that identify inappropriate access, recommend entitlements before users request them, and detect anomalous behavior across millions of identity events.
Lead other IAM team members including operations, onboarding, initiatives and engineering teams as well as line of business and lines of defense teams to understand analytical needs and translate them into technical solutions.
Establish and implement engineering and analytical best practices when developing solutions.
Develop solutions in accordance with established security, privacy, model risk, and regulatory guidelines.
Develop, test, deploy and support of ML-enabled analytical solutions and guide other members of the team in ML, AI and statistical techniques in driving adoption of AI/ML across IAM.
Assist in monitoring model health, reliability, and drift to ensure continuous improvement and required remediation.
Demonstrate proficiency in using AI‑assisted development and analysis tools (e.g., GitHub Copilot and approved code‑centric agents).
Leverage AI to accelerate system design, coding, testing, analysis, and troubleshooting.
Apply strong technical judgment when validating and integrating AI‑assisted outputs into solutions.
Understand and account for model limitations, security risks, and operational considerations.
Apply AI responsibly in development and production environments.
Ensure AI usage aligns with security, compliance, privacy, and ethical standards.