Data Scientist / AI Engineer, AVP

What we’ll offer you

Your key responsibilities

• Leadership (5%): Help drive the adoption of AI and analytics best practice across CBIBOC, IT (TDI), and Deutsche Bank more widely Stay abreast of industry trends and emerging technologies in data warehousing, business intelligence, and cloud analytics, particularly within the Google Cloud Platform ecosystem.
• Help drive the adoption of AI and analytics best practice across CBIBOC, IT (TDI), and Deutsche Bank more widely
• Stay abreast of industry trends and emerging technologies in data warehousing, business intelligence, and cloud analytics, particularly within the Google Cloud Platform ecosystem.
• Stakeholder Management and Governance (20%): Ensure AI governance requirements, as mandated by bank policy, regulation, or law, are adhered to for all new solutions developed Provide demos of new solutions, training materials, slides, and other materials as required to support the CBIBOC communication and upskilling agenda
• Ensure AI governance requirements, as mandated by bank policy, regulation, or law, are adhered to for all new solutions developed
• Provide demos of new solutions, training materials, slides, and other materials as required to support the CBIBOC communication and upskilling agenda
• Hands-on Development of AI Solutions (75%): Lead by example, working with other Squad members to design, develop, and implement novel AI solutions Partner with business stakeholders and other Squad members to understand their needs and priorities, and identify new opportunities for the development or use of AI solutions Ensure new AI use cases (Squad projects) are rigorously evaluated and prioritized based on expected ROI, strategic alignment, technical feasibility and risk Ensure all new AI solutions are thoroughly tested, validated, controlled (e.g. implementation of guardrails, access permissions, etc.), and monitored on an ongoing basis Ensure all new AI solutions are recorded in a centralized inventory and brought through the required approval steps prior to production implementation Track against agreed KPIs to demonstrate the effectiveness and value of each AI solution (e.g. costs and benefits as well as accuracy/consistency/performance)
• Lead by example, working with other Squad members to design, develop, and implement novel AI solutions
• Partner with business stakeholders and other Squad members to understand their needs and priorities, and identify new opportunities for the development or use of AI solutions
• Ensure new AI use cases (Squad projects) are rigorously evaluated and prioritized based on expected ROI, strategic alignment, technical feasibility and risk
• Ensure all new AI solutions are thoroughly tested, validated, controlled (e.g. implementation of guardrails, access permissions, etc.), and monitored on an ongoing basis
• Ensure all new AI solutions are recorded in a centralized inventory and brought through the required approval steps prior to production implementation
• Track against agreed KPIs to demonstrate the effectiveness and value of each AI solution (e.g. costs and benefits as well as accuracy/consistency/performance)

Your skills and experience

• Demonstrable experience using classical Machine Learning algorithms/models and/or cutting-edge AI tools to deliver new solutions into production, ideally at a tier 1 / G-SIB bank
• 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 data / feature engineering 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.
• 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.
• Proven ability to leverage AI tools to enhance productivity, optimise workflows to solve business problems, while applying critical judgment to ensure responsible and ethical use of data and AI outputs.

How we’ll support you

About us and our teams