Summer - CRE, Data Analyst Intern

Position: 2026 Summer - CRE, Data Analyst Intern (4 Months)

Job Description

What is the opportunity RBC Corporate Real Estate (CRE) is building a modern, data-driven workplace where AI supports better decisions across space, lease, occupancy, and cost management. Our goal is to move beyond static reports to intelligent systems that can analyze CRE data, reason across multiple sources, generate insights, and recommend actions for leaders and employees.

As an intern, you will have a chance to work at the intersection of advanced analytics, and agentic AI, helping design intelligent agents that sit on top of trusted CRE reports and datasets—turning reports into insights and insights into decisions.

You’ll work in a practical innovative environment where you can take ideas from “concept” to working proof-of-value with real stakeholders, real constraints (privacy, governance), and real impact.

This role offers hands-on exposure to CRE data, executive use cases, and RBC’s approach to responsible, governed AI innovation.
What you will do Build agentic AI prototypes that can plan tasks, call tools, and generate explanations, insights, and recommendations (e.g., “Why did occupancy costs increase?” or “Which buildings have underutilized space?”)Design and test agentic/GenAI workflows for CRE use cases

Work with structured CRE data to define agent inputs/outputs and ensure answers are grounded, auditable, and reproducible

Build evaluation and quality checks to validate agent responses against BI metrics and known sources of truth

Support analytics development including – data validation, defining and documenting metrics, translating business questions into analytical designs

Develop prompt/tool schemas and guardrails to ensure safe, accurate, policy-aligned outputs

Document and communicate findings through clear write-ups/business cases and lightweight demos for CRE stakeholders

Continuously learn and keep up with the latest advancements in AI and related technologies and sharing that knowledge with the team
What you need to succeed
Must Have Hands-on experience with Python (data handling, APIs, building prototypes)
Strong understanding and working knowledge of Machine Learning and Deep Learning algorithms

Strong understanding and working knowledge of LLMs and GenAI concepts, including prompt engineering, retrieval (RAG), function/tool calling, and structured outputs, prototyping GenAI applications, and vector databases (Weaviate,pgvector)
Familiarity with agentic patterns and implementation (planning + execution, multi-step reasoning, tool orchestration) and a desire to experiment and learn quickly

Ability to translate ambiguous business problems into technical approaches, and communicate trade-offs clearly

Strong analytics fundamentals, including: SQL proficiency

Understanding of KPIs, metrics, and variance analysis

Experience working with structured enterprise data

Data Modeling Curiosity, ownership, and comfort working with real-world constraints (data quality, security, stakeholder needs)
Strong logical thinking skills and attention to detail

Strong communication skills and comfort working with ambiguity
Nice to Have Working knowledge of embedding model fine-tunings and Model Context Protocol (MCP), LLM performance evaluation

Experience with agent frameworks (e.g., Lang Graph/Lang Chain, Semantic Kernel, Llama Index) or building multi-agent systems

Knowledge of cloud data platforms (Snowflake, Databricks, Data Iku)
Interest in Corporate Real Estate domains (space, occupancy, workplace experience, leasing, cost drivers)
UI prototyping experience (simple web apps, dashboards, Streamlit, or similar)
Familiarity with dashboard storytelling and executive-level insight presentation
What’s in it for you A chance to work on real agentic and GenAI initiatives inside a renowned enterprise, where your work can influence how leaders make CRE decisions

Hands-on experience combining BI, analytics, and AI—highly coveted marketable skill set Exposure to enterprise-scale CRE data and how analytics supports executive decision-making at RBCExposure to end-to-end delivery: use case definition → data grounding → AI design → evaluation → demo/storytelling

Mentorship from a team focused on AI strategy,…