Senior Quant Developer — Front-Office Data & Systems Engineer

Man Group; Boston (MA); Hybrid; USD 150,000 - 170,000; Full time

Benefits offered by this job

Two annual 'Mankind' volunteering days

Competitive benefits

Job summary

Man Group in Boston is seeking a Senior Quantitative Developer to join the Front-office Engineering team. You will work with Quant Researchers and Portfolio Managers on onboarding datasets, implementing trading signals, developing portfolio tools, and building data visualization frameworks.

Our Linux-dominated stack uses Python with NumPy, SciPy and Pandas, plus Java for high-throughput components, and databases such as MongoDB and MS SQL.

Qualifications

• 5-7 years of professional experience in software engineering, preferably with 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.

Responsibilities

• Onboard new datasets and implement trading signals.
• Develop portfolio optimization tools and data visualization frameworks.
• Enhance our research platform and tune performance of existing code.
• Collaborate with Quant Researchers and Portfolio Managers.

Skills

Python

Pandas

NumPy

SciPy

Git

Linux

Large data sets

Education

Bachelor's degree in CS/Math/Engineering/Physics

Tools

MongoDB

MS SQL

PostgreSQL

Docker

OpenStack

Kafka

Airflow

Grafana

Jenkins

Job description

Man Group in Boston is seeking a Senior Quantitative Developer to join the Front-office Engineering team. You will work with Quant Researchers and Portfolio Managers on onboarding datasets, implementing trading signals, developing portfolio tools, and building data visualization frameworks.

Our Linux-dominated stack uses Python with NumPy, SciPy and Pandas, plus Java for high-throughput components, and databases such as MongoDB and MS SQL.