Quant Developer

About Anthelion Capital

Anthelion Capital is an investment and data science platform that enhances fundamental investing through advanced data science techniques, applying these across the capital structure. The company is developing a proprietary platform that handles the complete investment lifecycle, from underwriting to portfolio management.

Role Overview

As a Quant Developer, you will fully own the quantitative engineering platform. Your responsibilities include building and maintaining the infrastructure critical to researchers and portfolio managers, such as a shared data layer, backtesting engines, model deployment pipelines, and monitoring systems to ensure live models perform accurately. Developers and researchers collaborate closely, writing against common systems so that your work is put to use immediately after release.

Key Responsibilities

• Design and maintain the shared data layer for market and reference data ingestion, feature and signal storage, and orchestration using Dagster, ensuring point-in-time correctness and self-service extensibility for researchers.
• Develop and enhance the backtesting and simulation tools.
• Create and refine portfolio construction and optimization libraries employed by portfolio managers for allocations.
• Implement a model deployment pipeline that enables seamless promotion of models from research to production through configuration.
• Establish monitoring and observability frameworks to detect model drift or failures promptly.

Additional Exposure

Engage with risk-factor modeling, exposure analytics, and provide direct support to portfolio managers involving strategy diagnostics, scenario analysis, and allocation inquiries.

Candidate Profile

• Open to new graduates and experienced professionals.
• Strong expertise in software engineering, particularly in Python and one or more systems programming languages, with good design skills and ability to develop dependable tools.
• Comprehensive understanding of quantitative finance concepts including Sharpe ratio, risk factors, backtesting methodology, and portfolio optimization, beyond implementation.
• Experience with data engineering best practices focusing on pipeline correctness, reliability, and point-in-time accuracy.
• A platform-oriented mindset focused on building reusable, well-guarded, self-service tooling rather than one-off scripts.
• Preferred but not required: experience with Dagster or Prefect, Azure cloud, model registries or feature stores, backgrounds in quantitative/trading firms or data platforms, and practical experience in risk modeling or portfolio construction.

Additional Information

• Compensation ranges from $120,000 to $240,000 annually commensurate with experience, plus eligibility for performance-based discretionary bonuses.
• Work location is onsite in Midtown, New York City, with a requirement to be present in the office at least three days per week.
• Candidates must be authorized to work in the United States without needing employer visa sponsorship.