Macroeconometric Analyst

at  Garda Capital Partners

Garda Capital Partners (Garda) is a multi-billion dollar alternative investment firm with over 20 years of experience deploying relative value strategies across fixed income markets for institutional investors. Garda has offices in Wayzata, New York City, West Palm Beach, Geneva, Zug, Copenhagen, Singapore, and Scottsdale.
Garda is looking to hire a highly skilled and motivated Macroeconometric Analyst with a strong background in Econometrics, preferably holding or currently pursuing a PhD in these fields, to work out of our Geneva office. The ideal candidate will possess in-depth knowledge of macroeconometrics, specifically in time-series analysis, forecasting models, and the manipulation of large datasets to predict market-relevant macroeconomic variables across various geographies. This role requires proficiency in high-level programming languages such as Matlab, R, Python, or Julia, and the ability to innovate by incorporating new data sources to enhance model predictions.

Position Responsibilities:

• Model Development: Design and implement models to forecast key macroeconomic indicators, such as U.S. Non-Farm Payrolls, adjusting predictions in real-time with incoming data.
• Data Analysis: Handle vast datasets, identifying and extracting meaningful insights while understanding concepts as seasonality, cointegration, and data revisions. This includes the acquisition of unconventional data through web scraping and negotiations with data providers.
• Communication: Clearly articulate model predictions and associated uncertainties to stakeholders, enabling informed decision-making for trading strategies.
• Market Insight: Analyze potential market reactions to economic indicators and provide actionable insights to traders for managing risks and opportunities.
• Continuous Improvement: Proactively seek and evaluate new data sources, delve into data construction details, and leverage changes in data methodology to refine model accuracy.
• Scalability: Enhance model management through automation, focusing on updating processes, predictions, visualizations, and communications to handle multiple models efficiently.

QUALIFICATIONS & DESIRED SKILLS:

• PhD in Economics/Econometrics with a focus on macroeconometrics (completed or ongoing).
• Expertise in time-series analysis, forecasting models, and the evaluation of model performance, including understanding risks such as overfitting.
• Proficiency in at least one high-level programming language (Matlab, R, Python, Julia) for model development and data analysis.
• Demonstrated ability to source, negotiate, and integrate new data into forecasting models.
• Knowledge of major macroeconomic data and the ability to leverage this knowledge to gain a competitive edge in model prediction.
• Excellent communication skills, with the ability to translate complex analytical findings into actionable insights for non-technical stakeholders.

• Model Development: Design and implement models to forecast key macroeconomic indicators, such as U.S. Non-Farm Payrolls, adjusting predictions in real-time with incoming data.
• Data Analysis: Handle vast datasets, identifying and extracting meaningful insights while understanding concepts as seasonality, cointegration, and data revisions. This includes the acquisition of unconventional data through web scraping and negotiations with data providers.
• Communication: Clearly articulate model predictions and associated uncertainties to stakeholders, enabling informed decision-making for trading strategies.
• Market Insight: Analyze potential market reactions to economic indicators and provide actionable insights to traders for managing risks and opportunities.
• Continuous Improvement: Proactively seek and evaluate new data sources, delve into data construction details, and leverage changes in data methodology to refine model accuracy.
• Scalability: Enhance model management through automation, focusing on updating processes, predictions, visualizations, and communications to handle multiple models efficiently