Quantitative Developer

Our client, a Boston-based investment management firm, is seeking a Senior Software Engineer to join their team that supports Quantitative Researchers and Portfolio Managers., They use Control-M and Airflow for workflow management, Kafka for data pipelines, Bitbucket for source control, Jenkins for continuous integration, Grafana + Prometheus for metrics collection, ELK for log shipping and monitoring, Docker for containerization, OpenStack for our private cloud, Ansible for architecture automation, and Slack for internal communication. Our technology list is never static: we constantly evaluate new tools and libraries.

Their systems are almost all running on Linux and most 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 systems that require the highest data throughput in Java. For storage, they rely heavily on MongoDB and MS SQL., * 2-3 years of professional experience in software engineering, preferably with a focus on quantitative applications \n

• Expert knowledge of Python and Pandas and proficiency with related scientific libraries including NumPy, SciPy, Statsmodels, and scikit-learn \n

• Experience developing mission-critical production systems, with knowledge of best practices for testing, monitoring, and deployment \n

• Proficient on Linux platforms and strong understanding of Git \n

• Working knowledge of one or more relevant database technologies, such as MS SQL, Postgres, or MongoDB \n

• Demonstrated experience working with large data sets, both structured and unstructured \n, * Experience in quantitative software development within a front-office setting, such as at a hedge fund, proprietary trading firm, or investment bank \n

• Experience mentoring junior team members and managing projects \n

• Experience building web applications using modern frameworks like React \n

• Proficient with distributed computing technologies such as Spark, Dask, Kubernetes, Redis \n

• Knowledge of modern data engineering practices including data pipeline & ETL tools, distributed storage & processing and data warehousing \n

• Strong understanding of financial markets and instruments \n

• Experience working with financial market data \n

• Relevant mathematical knowledge e.g., statistics, time-series analysis \n, * Strong academic record and a degree with high mathematics and computing content e.g., Computer Science, Mathematics, Engineering or Physics \n

• Intellectually robust with a keenly analytic approach to problem solving \n

• Self-organized with the ability to effectively manage time across multiple projects and with competing business demands and priorities \n

• Focused on delivering value to the business with relentless efforts to improve process \n

• Strong interpersonal skills: able to establish and maintain a close working relationship with quantitative researchers, portfolio managers, traders and senior businesspeople alike \n

• Confident communicator: able to argue a point concisely and deal positively with conflicting views \n