BNY Mellon has distinct expertise in trade execution as one of the world's largest asset managers and has the reputation as a trusted partner given our full suite of capital markets, financing and custody solutions.
Execution Services is a division of BNY Mellon that provides global multi-asset trading and execution related services to buyside clients. The team currently trades on behalf of $650b of our internal asset management AUM across equities, fixed income, derivatives and FX. Later this year, Execution Services will be offering this holistic trading solution to external clients as an outsourced service. Outsourcing Trading allows clients to benefit from our investment, experience and expertise in trading while they focus on their portfolio management and analytical core competencies.
JOB DESCRIPTION:
Exciting opportunity to join our team as a Data Scientist. This person will work with the quant research and engineering teams to build out our quant models as we work to improve decision support tools and recommendation systems in support of a global, multi-asset trading organization. Key responsibilities include:
Play a significant role in the design, research, development and deployment of a suite of contextual trading analytics.
Develop research quant libraries.
Create multi-asset class market impact models.
Develop real-time decision support tools.
Provide domain expertise in solving research-intensive portfolio implementation challenges.
Present research findings to internal and external clients and constituents.
Develop and maintain expert-level understanding of market micro structure globally and across asset classes.
Embrace and promote API-driven solutions, utilizing open source resources and cloud-based architecture.
Collaborate with internal teams and vendors to implement research in the production environment
PREFERRED QUALIFICATIONS:
Degree in computer science, statistics or similar - PhD highly preferred
3+ years' experience in research and data science roles
Solid coding skills in Python, R, C++ or Java
Keen understanding of REST, gRPC and other API-related frameworks, concepts and tools
Exposure to and comfort with high performance, low latency computing systems
Experience with real-time systems
Proficiency with AWS, Azure or other cloud-based environments
Handling real-time market data feeds across asset classes and regions
Deep knowledge of time series database technologies
Experience in PostgreSQL, Redis and other DB solutions
Expertise in ML and optimization technologies
Deep understanding of financial engineering and mathematical finance concepts
Collegial and collaborative disposition - team player!
Great presentation skills - be able to present research findings to a wide range of audiences
Advanced degree in quantitative analysis preferred.
12+ years total work experience preferred.
Experience in quantitative finance and technology preferred.
