Opportunity CLOSING DATE: September 26, 2026 Opportunity The Economics & Investment Research (E&IR;) is AIMCo’s in-house macro and investment research function. We exist for one purpose: to help the Chief Investment Officer, senior investment leadership, investment committees, investment teams and our clients make better-informed decisions about how capital is positioned. Our product is a combination of evidence-based analysis, judgement and the tools that sharpen both. The role Reporting to the Chief Economist and Head of Economics & Investment Research, the Director, Quantitative Investment Research is the senior quantitative voice within E&IR.; You will own the analytical frameworks behind how AIMCo’s E&IR; thinks about portfolio construction, risk budgeting, and asset allocation — and you will build the systematic tooling that turns those frameworks into something the CIO and investment committees can act on, cycle after cycle. This is a hands‑on Director role. You will spend a meaningful share of your time in code and data, and the rest translating results for people who will never open your notebook. You will help mentor a few quantitative analysts, setting the research agenda and the standards it is held to. The work is deliberately positioned close to the decision. Your analysis will not sit in a research library; it will be in front of the CIO, investment committees, as well as clients and it will be debated.
What this role is — and is not This advisory mandate is total client portfolio construction, risk, and decision support (not a systematic alpha seat for standalone trading P&L;). Candidates who want their work to change how CAD $200 billion is positioned will find it rewarding and purposeful.
What you will do Portfolio construction and asset allocation research
Own the advisory quantitative frameworks behind client total portfolio construction across CIO office risk optimization and capital allocation scenario analysis. Design and evaluate top‑down portfolio positioning and structure — and translate shifts in the macroeconomic regime into concrete, defensible positioning recommendations. Model systematic risk factors across the portfolio, forecast their behaviour, and quantify what holding a given top‑down view actually costs. Build and maintain the top‑down frameworks around benchmarking, currency hedging, and liquidity management. Develop capital market assumptions and the analytics that connect them to portfolio outcomes, including for private market exposures. Work within the real constraints of an asset owner: illiquidity, pacing, funding, and governance timelines — not a frictionless optimizer.
Systematic decision support for the Tactical Asset Allocation Committee
Build and own the systematic toolkit that supports the TAA Committee: regime and cycle models, valuation and momentum indicators, positioning and flow trackers, and complementary risk dashboards. Establish a repeatable cadence — the same evidence, produced the same way, every cycle — so that Committee debate is about judgement rather than about whose numbers are right. Frame conclusions probabilistically rather than as point forecasts, and be explicit about where consensus is strong, where it is thin, and where we differ. Design scenario narratives, with E&IR; and Risk Management colleagues,



with the transmission channels made visible: what the shock is, how it propagates, and which exposures it reaches first. Backtest and stress‑test proposed tactical tilts, and document both what the evidence supports and what it does not. Maintain an honest scorecard of past tactical decisions and model performance, and feed that record back into the process.
Risk analytics and stress testing
Develop top‑down macro factor risk decomposition, stress testing, scenario, and tail‑risk analytics spanning public and private asset classes. In collaboration with Risk Management and Multi‑Asset Portfolio Management colleagues, answer the questions leadership actually asks: how much of our risk is really one bet, what breaks in which regime, and what the marginal dollar of risk buys us. Partner with Risk Management as a first‑line analytical counterpart — complementing independent oversight rather than duplicating it.
Research platform, data, and model governance
Own the E&IR; quantitative stack: data pipelines, the research environment, code standards, and reproducibility. Set model governance standards — documentation, validation, benchmarking, version control, and periodic review — so that any model informing a decision is defensible to investment committees and Risk. Apply AI and large‑language‑model tooling deliberately and with judgement: accelerating research, processing unstructured macro and market information, and building governed interfaces to our own data. We are looking for demonstrated, critical application — not enthusiasm. Partner with Risk Management, Global Data and Technology, and the asset class teams to source and integrate data as needed, and to avoid rebuilding what already exists elsewhere in the organization.
Leadership and influence
Mentor and set the technical bar for a small team of quantitative analysts. Communicate complex quantitative concepts to technical and non‑technical audiences alike — the CIO, the TAA Committee, and other senior investment forums. Influence outcomes without direct authority, through analytical clarity rather than positional weight. Act as a credible internal counterparty to portfolio managers and asset‑class heads — able to challenge on method, and to be challenged in return.
What you will bring Required 10+ years in total portfolio strategy, asset allocation, multi‑asset portfolio construction, or investment risk analytics — at an asset owner, asset manager, bank, or hedge fund. A graduate degree (PhD, MFE, MSc, or equivalent) in a quantitatively rigorous discipline: mathematics, statistics, physics, financial engineering, econometrics, or computer science. Demonstrated command of portfolio theory, factor risk modelling, asset pricing, and optimization. Applied econometrics and time‑series methods: regime models, volatility modelling, simulation, and an honest understanding of what breaks out of sample. Expert Python,



and production‑quality engineering habits: version control, testing, documentation, reproducible research. A track record of influencing senior decision‑makers — CIOs, investment committees, boards — not merely of publishing research internally. The ability to hold a strong view under challenge, and to change it when the evidence changes.
Preferred Experience at a large asset owner (pension, sovereign wealth fund, endowment) with total portfolio rather than single strategy responsibility. Familiarity with private market assets in a total portfolio risk context: stale pricing, de‑smoothing, liquidity and pacing modelling, and private market capital market assumptions. Experience building decision‑support tools for an investment committee — not only research output. Exposure to optimization solvers and to cloud‑based research infrastructure. Macro or systematic strategy research experience: regime modelling, nowcasting, or macro factor construction. Demonstrated, practical use of AI/LLM tooling in a research or analytics setting. People‑management experience; CFA, FRM, or CAIA designation considered an asset. Technical environment Python; SQL; Git; Databricks; risk system — e.g. BlackRock Aladdin & MSCI BarraOne; Bloomberg, Macrobond; reporting layer — Power BI, Tableau, Streamlit, or Dash.
What success looks like in the first twelve months The TAA Committee receives a consistent, documented quantitative pack each cycle (monthly) — and uses it. Total portfolio risk and thematic decomposition and scenario analysis are reproducible on demand, rather than assembled ad hoc each time they are asked for. At least one portfolio construction framework has moved from prototype to production and has demonstrably changed a decision. A scorecard of past tactical decisions exists, is honest, and is discussed. The team’s research is version‑controlled, documented, and rerunnable by someone other than its author.
Location, work model, and compensation Location: Calgary. Work model: hybrid (3 days in office currently). This posting will close at 11:59 pm MST on September 25, 2026.
About AIMCo At AIMCo, we draw upon the differences in who we are, where we come from and the way we think to deliver results for the Albertans who rely on us. We offer an inclusive, up-to-date workplace where well‑being is prioritized, and colleagues are enabled to do their best work. Our team members are motivated by our purpose and committed to creating long‑term value for our clients and their beneficiaries. If you are a purpose‑driven, high‑achiever excited by the prospect of working in a dynamic industry, you’ll enjoy your career with us! AIMCo Alberta Investment Management Corporation (AIMCo) is one of Canada’s largest and most diversified institutional investment managers with more than CAN$194.7 billion of assets under management. AIMCo invests globally on behalf of multiple pension, endowment, insurance and government funds in the Province of Alberta. AIMCo prioritizes results and outcomes through a flexible, hybrid approach to work. We are looking for proven achievers, motivated to work in a collaborative environment to help our clients secure a better financial future for the Albertans they serve.

#J-18808-Ljbffr

📌 Director, Quantitative Investment Research (Alberta)
🏢 Alberta Investment Management
📍 Alberta