Quantitative Developer

Alpha Analitica; United States; Remote; USD 90,000 - 130,000; Full time

Job summary

A financial analytics firm is seeking a remote Quantitative Developer to enhance trading systems and risk management tools. This role involves programming in Quantlib and Python, along with implementing quantitative models for fixed income markets. The ideal candidate will work closely with traders and quantitative researchers, translating models into production code. Strong knowledge of financial instruments and automated trading platforms is crucial, alongside experience with libraries like NumPy and Pandas.

Qualifications

• Strong programming skills in Quantlib and Python.
• Experience with R or MATLAB is a plus.
• Understanding of financial data formats and market data feeds.

Responsibilities

• Design and maintain trading systems and quantitative models.
• Collaborate with researchers to implement mathematical models.
• Integrate with external data vendors and support trading desk operations.

Skills

Strong proficiency in Quantlib

Python

R

MATLAB

Quantitative Libraries

Financial Instruments understanding

Options pricing models

Risk metrics

Tools

Git

SVN

Job description

Alpha Analitica specializes in self-funding analytics software that helps financial institutions reduce market data costs by up to 50%, allowing significant savings without compromising access or performance. By eliminating excessive Bloomberg "DERIVED" data expenses, the company offers a scalable solution that integrates seamlessly into existing workflows across teams and geographies. Recognized for its operational efficiency and cost-control capabilities, Alpha Analitica provides innovative tools for financial firms to optimize their market data spending. These solutions address the growing need to maximize the utility of market data, one of the largest recurring costs in the financial industry.

Role Description

This is a remote, contract role for a Quantitative Developer. We are seeking a skilled Quantitative Developer to join our trading technology team. This role combines quantitative analysis, software development, and financial markets expertise to build and maintain systems that support our trading strategies, risk management, and portfolio optimization efforts. The ideal candidate will have strong programming skills, deep understanding of Fixed income markets, and experience with quantitative modeling techniques.

Key Responsibilities

Development & Implementation

• Design, develop, and maintain high-performance trading systems and quantitative models
• Build automated trading platforms, execution algorithms, and order management systems
• Implement pricing models, risk metrics, and portfolio optimization tools

Quantitative Analysis

• Collaborate with quantitative researchers to translate mathematical models into production code
• Implement statistical models, machine learning algorithms, and econometric techniques
• Build and maintain derivatives pricing engines and volatility models
• Develop risk management tools including VaR, stress testing, and scenario analysis
• Create performance attribution and portfolio analytics systems
• Integrate with external data vendors (Bloomberg, Reuters, exchanges)
• Work closely with traders, portfolio managers, and quantitative researchers
• Collaborate with infrastructure teams on deployment and production support
• Participate in code reviews, testing, and documentation processes
• Support trading desk operations and troubleshoot production issues

Required Qualifications

Technical Skills

• Programming Languages: Strong proficiency in Quantlib and Python, with experience with R, MATLAB, or similar
• Financial Libraries: Experience with QuantLib, NumPy, Pandas, SciPy, or similar quantitative libraries
• Market Data: Knowledge of financial data formats (FIX protocol, market data feeds)
• Version Control: Git, SVN, or similar version control systems
• Strong understanding of financial instruments (fixed income)
• Knowledge of options pricing models (Black-Scholes, binomial trees, Monte Carlo methods)
• Understanding of risk metrics (Greeks, VaR, expected shortfall)