Quantitative Strategist
@ HelixRecruit

Hybrid
$100,000
Hybrid
Part Time
Posted 4 days ago

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Job Details

Overview

Join HelixRecruit's partner on a frontier research project with a leading AI lab. As a Quantitative Strategist, you will contribute high-quality financial forecasts and structured reasoning to improve AI prediction systems.

Key Responsibilities

  • Generate probabilistic forecasts on financial markets, instruments, and macro indicators
  • Document quantitative reasoning, models, and assumptions
  • Analyze historical data and identify signals across asset classes
  • Calibrate predictions with peers in economics and finance
  • Refine forecasting methodologies and inputs

Ideal Qualifications

  • Background in quantitative finance, trading, economics, or data science
  • Experience in financial forecasting or market modeling
  • Proficiency with Python, R, or Excel for data analysis and backtesting
  • Forecasting competition participation (e.g., Numerai, QuantConnect) is a plus
  • Strong written communication and analytical skills

Opportunity Details

This is a remote, asynchronous role with an expected commitment of approximately 10–20 hours per week. You will be engaged as an independent contractor.

Compensation & Application Process

The rate is $105–$140 per hour for U.S.-based applicants. Submit your profile or resume to start, complete a brief form, and follow-up will occur within a few days with next steps.

Key skills/competency

  • Quantitative Finance
  • Forecasting
  • Statistical Modeling
  • Trading
  • Data Analysis
  • Python
  • R
  • Excel
  • Research
  • Communication

How to Get Hired at HelixRecruit

🎯 Tips for Getting Hired

  • Tailor your resume: Highlight quantitative finance expertise and forecasts.
  • Showcase projects: Include trading or modeling accomplishments.
  • Research HelixRecruit: Understand company and partner profiles.
  • Prepare examples: Demonstrate data analysis and forecasting skills.

📝 Interview Preparation Advice

Technical Preparation

Review financial forecasting models.
Practice Python and R data analysis.
Study historical market data trends.
Brush up on statistical model calibration.

Behavioral Questions

Describe a complex project challenge.
Explain your teamwork experience briefly.
Discuss a time you adapted quickly.
Detail how you manage deadlines.

Frequently Asked Questions