Job Description:
Systematic Macro Quantitative Researcher/ Analyst
Role SummaryWe are seeking a Systematic Macro Quantitative Analyst to support the research, development, and implementation of systematic macro strategies across global markets. The strategy universe includes currencies (FX), commodities, sector ETFs, and index futures (and related derivatives). This role is designed for a hands-on quantitative researcher who can take ideas from hypothesis to live trading, combining strong statistical foundations with efficient Python and SQL skills. We are not hiring a software developer; however, the successful candidate must write clean, performant analytical code and contribute meaningfully to research tooling and production workflows.
You will work closely with the Senior Portfolio Manager to conduct alpha research, build and validate models, improve data pipelines, and enhance trading infrastructure (including order generation and execution optimization). The role requires macro market exposure, strong financial markets intuition, and either prior experience or strong interest in macro markets.
Key Responsibilities1) Alpha research and signal development
Generate and evaluate trading ideas using statistical methods and time-series analysis across macro asset classes.
Perform feature engineering, exploratory data analysis, and robust out-of-sample testing to mitigate overfitting.
Contribute to factor research relevant to systematic macro (e.g., trend, carry, value, momentum, volatility, defensive/risk-on-off dynamics).
Analyze factor interactions across assets and regimes; identify robust signals and diversification benefits.
Document research findings in a clear and reproducible manner (research notes, methodology, and assumptions).
2) Portfolio Construction
Develop portfolio construction and optimization approaches (position sizing, constraints, risk targeting, and diversification).
Incorporate transaction costs, slippage, liquidity, and market impact considerations into model design and backtests.
Translate research prototypes into reliable, repeatable implementations suitable for live trading workflows.
3) Data processing and portfolio analytics
Ability to work with large, often unconventional datasets (market, fundamental, positioning, macro indicators, alternative data).
Strong knowledge of SQL a must. Familiarity with Mongo DB, data platforms like Snowflake, a plus.
Ability to onboard new datasets, identify and clean data issues.
Leverage data-driven tools for research workflows, diagnostics, and portfolio optimization.
Build repeatable analytics for performance attribution, risk decomposition, and scenario analysis to support portfolio decisions.
4) Collaboration and communication with investment and engineering teams
Collaborate closely with portfolio managers, traders, and quant engineers throughout the research-to-production lifecycle.
Explain models, signals, and backtest results in clear language; communicate key drivers, limitations, and risks.
Participate in team design reviews and research discussions; provide transparent, well-reasoned recommendations.
5) Trading infrastructure: data, backtesting, and execution
Contribute to improving trading infrastructure, including data ingestion and quality checks, research datasets, and monitoring.
Enhance internal backtesting frameworks, simulation tooling, and experiment tracking to ensure reproducibility and robustness.
Support order generation, pre-trade controls, and execution optimization in coordination with trading and engineering partners.
6) Partner with the Senior Portfolio Manager on systematic macro strategy development
Work directly with the Senior Portfolio Manager to develop systematic macro strategies, focusing on alpha research from idea generation through implementation.
Own components of the research process: data preprocessing, statistical analysis, backtesting, validation, and deployment support.
Independently explore and develop new alpha ideas while collaborating in a transparent, team-oriented environment.
Bachelor's or Master's degree in a quantitative discipline such as applied mathematics, statistics, financial engineering, computer science, physics, or a related field.
Strong understanding of financial markets with macro focus (FX, commodities, futures, ETFs, indices) and familiarity with derivatives mechanics and market conventions.
Prior experience in systematic macro, multi-asset quant, CTA, global macro, or a closely related quantitative investing environment.
Excellent Python skills (exposure to numerical libraries like pandas, numpy, scipy, statsmodels, scikit-learn) with the ability to write efficient, maintainable research code and implement data pipelines and analytical tooling (not a pure developer role, but production-quality analytical code is required).
Strong SQL skills, including ability to write performant queries and work with large datasets (joins, aggregations, window functions, query optimization basics).
Solid foundation in statistics and time-series analysis; ability to design experiments, validate results, and communicate uncertainty.
Familiarity with portfolio optimization tools; experience with backtesting frameworks and evaluation methodologies.
Strong communication skills and a collaborative style.
Scientific mindset: hypothesis-driven research, rigorous validation, and disciplined documentation.
Ownership and accountability: drives work to completion with attention to quality and controls.
Communication and influence: distills complex analysis into clear decisions and trade-offs.
Collaboration: works effectively across PM, trading, and engineering stakeholders.
You have delivered multiple robust signals or model and trading enhancements that are integrated into systematic macro strategies.
Research and backtesting outputs are reliable, well-documented, and trusted by PMs and trading partners.
Data pipelines and quality checks are improved to reduce operational friction and increase confidence in inputs.
You communicate clearly and proactively, enabling faster decision-making and better portfolio outcomes.
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