Asset Management- Equities Quantitative Developer - Vice President/Associate

JPMorgan Chase & Co.•New York, NY

1d•$170,000 - $285,000

About The Position

The Quantitative Developer/Engineer is expected to design, develop, deploy and operate innovative data pipelines and quant applications to impact the team’s alpha generation for asset management clients. You will help implement the research agenda of the U.S. Disciplined Core Equity group and enhance the production processes. The developer will also work on research projects in partnership with other researchers.

Requirements

3+ years of experience in relevant fields
Degrees in Computer Science or Engineering
Proficiency in Python programming, including familiarity with multithreading and multiprocessing; database management experience across SQL, NoSQL, and Snowflake; familiarity with ETL pipelines; and experience architecting applications within AWS
Familiarity with Git-based version control and collaborative software development workflows
Experience with Airflow or similar workflow orchestration tools for production data pipelines
Statistics and finance knowledge, especially within equity asset management, quantitative equity investing, or portfolio construction
Good communication and project management skills
Experience in building sophisticated automated processes and technology infrastructure

Nice To Haves

Experience with tax-aware long-short optimizations and implementation
Experience with vendor optimization platforms and packages, such as MSCI Barra Open Optimizer, for portfolio construction, tax optimization, and optimization workflows
Experience in designing processes used in financial services
Knowledge of Machine Learning, Natural Language Processing, and other unstructured data
Experience in building pipelines for ML inference based on text, timeseries or financial data
Familiarity with statistical packages such as Matlab

Responsibilities

Lead the management of production processes and daily communication with technology team to ensure production pipeline is functioning as expected
Translate research models into production processes
Automate generation of reports for portfolio managers
Enhance the efficiency of the research processes such as improving alpha model estimation and optimized backtesting pipeline
Onboard new data sets and conduct exploratory analysis and manage existing data sets used in research
Lead development of highly sophisticated end-to-end ML pipelines in research, which can be deployed in production environment easily
Conduct research projects in quantitative equity investment