James Kidd.
I design reliable data systems and internal tools
Internships
Distribution Enablement Co-op
May 2026 — PresentBuilt data-connected Seismic LiveDocs templates via a .NET application plugin, SQL, and Cinchy to automate the population of fund performance and $450B+ AUM data. Executed this workflow under the guidance of the enablement team, ultimately saving the Equities group 40+ hours monthly.
Created a tracking system using NLP and Excel key-sort logic to map disparate Seismic content IDs to client interaction URLs. This tooling provided supervisors with actionable KPIs on optimal sending periods and engagement.
Supported the ETF and Mutual Fund advisory merger, as well as the Invesco Canada transition, by designing a new content schema. Reorganized universal SharePoint libraries to improve team accessibility and help prepare the firm’s data environment for Microsoft Copilot deployment.
Investment Advisory Analyst Co-op
Jan 2026 — Apr 2026Identified a compliance bottleneck regarding Quebec language laws and proactively proposed an automated workflow using Morningstar bulk data and custom glossaries. Developed the script to reduce a 146-page manual translation task to 34 automated pages, ensuring 34 Know-Your-Product documents met compliance ahead of the deadline.
Assisted the portfolio management team by researching and synthesizing capital flow trends. Contributed foundational research for PM-published macro-commentaries on AI infrastructure credit yields, legacy SaaS disruption, and private markets software resets.
Drafted evergreen positioning collateral for Global Alpha Innovators, Emerging Markets, and Small Cap funds. Worked closely with advisors to help bridge portfolio manager strategy with materials used for global sales distribution.
Software Developer Intern
May 2024 — Sep 2024Developed Python automation scripts for data migration between production and test systems. Built data extraction and order conversion tools to enhance Jira-based workflows, and implemented analytics tracking utilizing Meta Pixel and Google Analytics.
Technology Enablement Advisor Intern
Jun 2021 — Sep 2021Assisted in analyzing operational workflows to identify automation opportunities. Proposed and designed UiPath workflows, delivering actionable RPA recommendations to senior advisors.
Leadership
President & Strategic Visionary
2024 — PresentProgressed from a participant (2023) to Logistics Lead (2024–2025), and ultimately President (2026) of McGill's largest engineering hackathon.
Working alongside the executive committee, I helped pivot the competition's focus from standard software prototyping to an applied AI systems laboratory. Coordinated with enterprise partners like IBM to integrate their technologies into the event, designing technical challenge tracks to evaluate 400+ students on multi-agent orchestration, token budgeting, and responsible AI governance.
Director of Sponsorships
2021 — 2022Managed sponsor communications and external relations for Queen's University's annual cybersecurity hackathon, helping secure the funding required to support competition operations and event growth.
Freelance
Technology Advisor
2024 — PresentAdvised executive leadership on architecture transitions and API integrations to modernize legacy ERP/CRM workflows for Marquis Logistics and Elevate Financial.
Graduating from McGill with a degree in Mathematics and Computer Science. Over the last few years I've split time between capital markets at CI Financial and building software systems — automation tooling, data pipelines, NLP workflows. I've also run McGill CodeJam as President, which meant coordinating 400 students, enterprise partners, and a competition pivot toward applied AI evaluation.
My coursework grounded me in the theory that changes how you actually think: statistical learning, algorithm design, information theory, graph theory. The internships showed me where that theory runs into messy data and real business constraints. The interesting work lives in that gap.
I take a principled approach to quantitative methods. In finance, that means factor-based frameworks over narrative-driven investing. In ML, it means interpretable models and honest evaluation over benchmark chasing. I've watched AI get oversold in environments where I've also seen it genuinely deliver — the difference is usually whether the method is rigorous or just convenient.
Looking for a role where the technical bar is high, the domain is quantitative, and the track has room to grow.
Education Timeline
McGill University
2023 — 2026Concentration in data systems, statistical learning, automation, and algorithmic problem-solving.
Queen's University
2020 — 2022Strong foundation in engineering mathematics, computer architecture, and low-level systems programming.
Skills & Expertise
Backend engineer with full-stack capability, AI and data science depth, and financial markets exposure.
Systematic
Systems-driven, organized, and execution-focused
- Designed automated regulatory translation workflows for 34 financial funds
- Built production data migration pipelines and structured RPA workflows
- Comfortable bridging high-level business requirements with technical implementation
Forward-Looking
Adaptable, curious, and grounded in what actually works
- Pivoted McGill CodeJam as President to focus on applied AI governance and orchestration
- Built AI-powered tools ranging from NLP scheduling to computer vision pipelines
- Both an AI adopter and a thoughtful skeptic — focused on commercial value, not hype
Quantitative
Depth in mathematics, statistics, and financial analysis
- Applied ML coursework encompassing PyTorch, classical ML, and NLP pipelines
- Algorithm design and complexity analysis across competitive and academic settings
- Supported fundamental research across global equities and private markets at CI GAM
Languages
Selected Projects
Managerial DNA: The Structural Equivalence of Active Management
FeaturedA quantitative framework that classifies managerial style by replacing static marketing labels with dynamic behavioral signatures derived from regime mapping and graph theory.
Deterministic Vision Pipeline
FeaturedLive DemoA classical computer vision pipeline that segments imagery into discrete color regions without neural networks. Currently being applied to satellite vegetation tracking for agricultural yield prediction. Paint-by-Number is the sandbox.
jameskidd.info
A modular, data-driven React application built to cleanly present quantitative research, software systems, and professional experience.
SquashNoFriends
'23 — First pseudo-AI agentSquash matchmaking platform built at McGill CodeJam. Fed an LLM ambiguous schedule descriptions and watched it return structured data. That was the moment.
Offline Mode
There's life beyond coding. As much as I enjoy building software, I value time spent outdoors, traveling, and real human connection.