Data Science and AI Engineering Lead, VP

Role Description

Driving the creation of new AI solutions for Corporate Banking and Investment Banking Operations and Controls (CBIBOC).As Deutsche Bank enters into a phase where a variety of data and AI development tools are becoming more accessible, we are seeking an experienced Data Scientist and AI Engineer to help drive the implementation of the Corporate Banking and Investment Banking Operations and Controls.

Skills and experience

• Demonstrable experience using both classical Machine Learning algorithms/models and cutting-edge AI tools to deliver new solutions into production at a tier 1 / G-SIB bank, ideally in the context of Operations or Control functions (e.g. KYC, AML, surveillance)
• Demonstrable experience using Data Science / AI developer tools and platforms such as Github, VS Code, PyTorch, TensorFlow, scikit-learn, MLflow, Weights and Biases, Jupyter Notebook, Hugging Face, Langchain, Langgraph, Google Vertex, Neo4J, etc.
• Experience using no-code / low-code AI tools to rapidly generate new AI and automation solutions (e.g. Microsoft AI Builder / Power Automate, Google AI Studio, Claude Cowork, etc.)
• Experience with front-to-back AI solution design and architecture, particularly the design of agentic AI solutions
• Demonstrable experience with data / feature engineering, data product creation, and modern data solutions such as BigQuery, Looker, PostgreSQL, Pinecone, Spark, Clickhouse, MongoDB, Redis, Databricks, Denodo, Snowflake, etc.
• Strong knowledge of current best practice for AI risk management across the solution lifecycle, and how to leverage technology to implement these controls (e.g. from development, testing, and validation of new solutions to setting access controls/permissions, implementing guardrails, and automating performance monitoring and reporting / dashboards)
• Ability to write / review Python code and experience using AI coding assistants (Claude Code, Github Copilot, etc.). SQL and Java experience desirable but not essential.
• Experience developing production-grade software within a Software Development Lifecycle (SDLC) control framework
• Strong stakeholder management, collaboration, and communication skills (both written/visual communications, e.g. data visualization and PowerPoint presentations, and verbal)
• The ability to balance people management, hands-on development work, and governance activities in a flexible day-to-day manner.
• The desire to grow and learn, keep up with the latest developments and new AI offerings emerging across the industry, and progress their Data Science / AI career.