Quantitative Software Engineer (DV Commodities)

About Us:

DV Trading, LLC is a proprietary trading firm with over 200 employees trading equities, fixed income, FX and commodity products. DV Commodities is a rapidly growing division that specializes in trading crude oil, refined products, natural gas, and other commodities across US, European, and Asian markets. Our combined risk management, world class technology, and human capital have enabled us to become one of the largest liquidity providers within the global energy complex.

Overview:

DV is looking for a software engineer with a quantitative mindset to join a small team building the tools that power our commodities trading. You will work directly with traders and quants to turn ideas into production-grade quant trading tools, fundamental analysis models, and analytics, and you will own them from prototype through daily use.

Job Responsibilities:

• Work alongside traders, quants and other engineers to build quant trading tools, including signal research, backtesting frameworks, and strategy monitoring
• Develop and productionize fundamental models for energy markets, such as supply/demand balances, inventory and flow analysis, and curve and spread models
• Build analytics and visualization tools that give traders clear, timely views of markets, positions, and PnL drivers
• Design data pipelines that ingest, clean and store market, fundamental, and alternative data from internal and third-party sources
• Create efficient storage and access schemes for time series, reference, and fundamental data
• Integrate models and tools with our event-based risk, market data and pricing platform
• Test, monitor and document models and tools, and iterate quickly based on trader feedback

Requirements:

• Bachelor’s degree or higher in CS, Engineering, Mathematics, Statistics, Physics or another quantitative discipline
• 2+ years professional experience building quantitative trading tools, models, or analytics in a financial or commodities setting
• Strong Python skills in a scientific computing environment (numpy, pandas, scipy, statsmodels or similar)
• Solid grounding in statistics, time series analysis, and data modeling; machine learning experience is a plus
• Experience building data pipelines and working with large data sets • Experience with SQL, NoSQL or tick databases
• Experience with data visualization and dashboarding tools (e.g., Plotly/Dash, Streamlit, Grafana)
• Strong knowledge of algorithms, design patterns, and OOP; Java or C++ is a plus
• Experience working in a Unix environment and git
• Familiarity with Kafka, Docker, Airflow, or Luigi
• Strong communication skills in verbal and written English, with the ability to work closely with traders
• Domain knowledge in energy markets, futures and swaps is a plus