Our client, a leading systematic trading firm, based in Boston, is seeking a Senior Quant Engineer to join their team.

As a Senior Engineer in the Front-office Engineering organization, you will work closely with Quantitative Researchers and Portfolio Managers. Your challenges will be varied and may include onboarding new datasets, implementing new trading signals, developing portfolio optimization tools, building data visualization frameworks, enhancing our research platform, and performance tuning existing code using efficient numerical algorithms and cluster-computing solutions.

Their systems are almost all running on Linux and most of the code is in Python, with the full scientific stack: NumPy, SciPy, Pandas, Statsmodels, and Scikit-learn to name a few of the libraries we use extensively. They implement the systems that require the highest data throughput in Java. For storage, they rely heavily on MongoDB and MS SQL.

In order to qualify:

• 5-7 years of professional experience in software engineering, preferably with a focus on quantitative applications
• Expert knowledge of Python and Pandas and proficiency with related scientific libraries including NumPy, SciPy, Statsmodels, and Scikit-learn
• Experience developing mission-critical production systems, with knowledge of best practices for testing, monitoring, and deployment
• Proficient on Linux platforms and strong understanding of Git
• Working knowledge of one or more relevant database technologies, such as MS SQL, Postgres, or MongoDB
• Demonstrated experience working with large data sets, both structured and unstructured

Nice to have:

• Experience in quantitative software development within a front-office setting, such as at a hedge fund, proprietary trading firm, or investment bank
• Experience mentoring junior team members and managing projects
• Experience building web applications using modern frameworks like React
• Proficient with distributed computing technologies such as Spark, Dask, Kubernetes, Redis
• Knowledge of modern data engineering practices including data pipeline & ETL tools, distributed storage & processing and data warehousing
• Strong understanding of financial markets and instruments
• Experience working with financial market data
• Relevant mathematical knowledge e.g., statistics, time-series analysis