Investment Strategist responsible for market analysis and developing investment theses at a fintech firm. Engaging with data and presenting findings to stakeholders.
Responsibilities
Thematic Research: Identify and investigate emerging market trends, structural shifts, and inefficiencies across asset classes
Independent Idea Generation: Proactively develop "top-down" and "bottom-up" investment theses. You will be expected to bring fresh, non-consensus ideas to the table.
Quantitative Validation: Build and maintain proprietary models to backtest investment hypotheses. You will provide the "proof" for every view, using factor analysis, regime detection, and econometric modeling.
Articulating Views: Translate complex quantitative outputs into persuasive investment narratives. This includes writing research notes, white papers, and presenting at weekly investment briefings.
Portfolio Impact Analysis: Evaluate how proposed views impact total portfolio risk, correlation, and expected return profiles.
Data Architecture: Maintain an independent research stack (Python/SQL) to automate the monitoring of key investment signals and "early warning" indicators.
Requirements
Experience: 3–5 years of experience in investment research, strategy, or portfolio management.
Education: Master’s degree or higher in a quantitative discipline (Finance, Economics, Mathematics, or Physics). Technical Proficiency: Advanced Python or R for data analysis and visualization (Matplotlib, Plotly, Seaborn).
Expertise in Bloomberg/Refinitiv for data extraction and market monitoring.
Solid understanding of SQL for handling large financial datasets.
Market Knowledge: Deep understanding of macroeconomics, capital market assumptions (CMA), and factor-based investing.
Communication: Exceptional written and verbal communication skills. You must be able to explain $R^2$ and $p$-values to a non-technical audience without losing the nuance of the investment view.
Preferred Attributes
Intellectual Independence: A track record of challenging the "house view" with data-backed evidence.
Narrative Flair: The ability to find the "story" within the data.
Pragmatism: An understanding that a model is only as good as its real-world tradability.
CFA designation or progress toward it is preferred.
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