Quantitative Software Engineer (DV Commodities)

DV Trading LLC; Greater London; Sur place; GBP 70 000 - 120 000; Plein temps

Avantages offerts par ce poste

Discretionary bonus

Medical, dental, and vision insurance

HSA/FSA options

Life/AD&D insurance

Résumé du poste

DV Trading LLC is seeking 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.

The role involves developing and productionizing energy-market models, building robust data pipelines, and delivering clear visualizations to

Qualifications

• Bachelor's degree or higher in a quantitative discipline.
• 2+ years experience building quantitative trading tools, models, or analytics.
• Strong Python in a scientific computing environment (numpy, pandas, scipy, statsmodels).
• Solid grounding in statistics, time series analysis, and data modeling.
• Experience building data pipelines and working with large data sets.
• Experience with SQL, NoSQL or tick databases.
• Experience with data visualization and dashboards (Plotly/Dash, Streamlit, Grafana).
• Strong knowledge of algorithms, design patterns, and OOP; Java or C++ is a plus.
• Experience in Unix environment and git.
• Familiarity with Kafka, Docker, Airflow, or Luigi.
• Strong communication skills in verbal and written English.
• Domain knowledge in energy markets is a plus.

Responsabilités

• Collaborate with traders, quants and engineers to build quant trading tools, backtesting, and strategy monitoring.
• Develop and productionize fundamental models for energy markets (supply/demand, inventory, flow analyses).
• Build analytics and visualization tools for traders to view markets, positions and PnL drivers.
• Design data pipelines ingesting and cleansing market, fundamental and external data.
• Create efficient storage/access for time series and reference data.
• Integrate models/tools with risk, market data and pricing platforms.
• Test, monitor and document models; iterate based on trader feedback.

Connaissances

Python (NumPy/SciPy)

Statistics

Time series analysis

Data visualization

Algorithms & OOP

Unix & Git

Communication in English

Formation

Bachelor's degree or higher in CS/Engineering/Math/Stats/Physics

Outils

Plotly/Dash

Streamlit

Grafana

Kafka

Docker

Airflow or Luigi

Description du poste

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

Benefits

• Discretionary bonus eligibility
• Medical, dental, and vision insurance
• HSA, FSA, and Dependent Care Options
• Employer Paid Group Term Life and AD&D insurance
• Voluntary LTD, Life & AD&D insurance
• Flexible Vacation policy
• Retirement plan with employer match

DV is not accepting unsolicited resumes from search firms. Only search firms with valid, written agreements with DV should submit resumes in response to DV's posted positions. All resumes submitted by search firms to DV via e-mail, the Internet, personal delivery, facsimile, and any other method without a valid written agreement shall be deemed the sole property of DV, and no fee will be paid in the event the candidate is hired by DV. DV is proud to be an equal opportunity employer and committed to creating an inclusive environment for all employees.