Calum Kershaw

I am an aspiring AI & Data Analyst focused on learning, practicing, and stress-testing modern AI, data, and automation tools through real-world use, experimentation, and curiosity-driven projects.

My foundation comes from hands-on operational work in a live service environment. In my current role at Jollytails Resort, a multi-service dog daycare, boarding, grooming, and retail operation, I work closely with scheduling, staffing, reporting, and day-to-day operational decision-making. This environment has given me firsthand experience with messy data, competing priorities, and the need for tools that support quick, practical decisions rather than theoretical perfection. It strongly influences how I approach AI and analytics: as tools that must fit real workflows and real constraints to be useful.

I actively use AI and data tools to support internal reporting, operational analysis, and process improvement. This includes experimenting with dashboards, summaries, and automation workflows that turn raw operational data into clearer signals for decision-making. These projects are part learning exercise, part business support, and part ongoing experimentation.

In parallel, I explore AI through creative and systems-based projects, including the design and development of tabletop role-playing game (D&D) systems, tools, and world-building frameworks. Designing game systems has strengthened my understanding of feedback loops, resource constraints, incentives, and emergent behavior—concepts that translate directly to analytics, optimization, and system design. I often use AI tools to prototype mechanics, manage complex rule sets, generate structured content, and test system balance.

My learning approach emphasizes applying tools in live contexts rather than isolated demos. I intentionally test AI in operational settings, side projects, and creative experiments to understand not just what works, but where tools break down, create friction, or require human judgment. Identifying limitations and trade-offs is a core part of my practice.

I am particularly interested in how AI can support small teams, service businesses, and operational roles that lack dedicated analytics resources. Many organizations don’t need more data—they need simpler systems, clearer insights, and tools that align with how people actually work. My goal is to develop the judgment and technical fluency needed to build those kinds of systems responsibly.

I also believe learning should be sustainable and enjoyable. Alongside professional development, I intentionally use AI for fun, exploration, and creative problem-solving. Play, experimentation, and curiosity-driven projects help me build intuition, maintain momentum, and discover unexpected applications of tools.

I am transparent about my stage of growth. I am not positioning myself as an expert or consultant, but as a motivated practitioner learning in public and building depth through repetition, reflection, and real-world use. I document learnings, refine approaches, and continuously adjust as tools evolve.

Over time, I aim to grow into an AI & Data Analyst role that combines analytical rigor, operational context, and thoughtful AI use. Until then, this profile reflects an ongoing journey of hands-on learning, experimentation, and applied practice across both business and creative domains.

Calum Kershaw is an aspiring AI & Data Analyst based in Halifax with a distinctive approach: testing tools where they actually break. Working in live operations at a multi-service dog care facility, they build dashboards, automation workflows, and reporting systems that turn messy real-world data into actionable decisions.

Their assessment revealed exceptional synthesis abilities and genuine debugging skill, demonstrating someone who has moved from theorizing about systems to actually building them. They think in feedback loops and constraints, a perspective sharpened through an unexpected practice ground: designing tabletop RPG mechanics where balance and emergent behavior are everything.

What makes Calum worth watching: they're building exactly the judgment that resource-strapped small teams desperately need, and they're doing it transparently, in public, one real problem at a time.