Senior Quantitative Developer

Fidelity•Boston, MA

8h•Hybrid

About The Position

Position Description : Builds high quality, robust, and efficient analytical solutions to improve internal investment processes with quantitative techniques and methods, statistics, and econometrics – including probability, linear regression, and time series data analysis. Participates in full stack development projects on the frontend User Interface (UIs) and back-end Application Programming Interfaces (APIs), using Python and SQL. Applies sophisticated analytics and quantitative concepts to support investment needs and develops new solutions. Adds scale, rigor, and repeatability to research through software development standard methodologies. Applies data mining, data modeling, natural language processing, and Machine Learning (ML) to extract and analyzes information from large structured and unstructured datasets. Primary Responsibilities: Develops analytical solutions. Develops and monitors portfolio personalization tool and efforts. Enables users to run customized portfolio optimizations based on personal inputs. Ensures all coding test cases are properly defined. Reviews and ensures code format and quality satisfy the general engineering standard, and code documentations are sufficient. Builds high quality, robust, and efficient analytical solutions to improve investment processes. Analyzes and interprets statistical data to identify differences in relationships among sources of in-formation. Evaluates technical data to determine effect on designs or plans. Identifies relationships and trends in data to determine effects on research results. Develops and implements complex techniques or analytics applications to transform raw data using data-oriented programming languages and visualization software. Provides high level technical support to production applications to ensure their stability, performance, and reliability. Education and Experience : Bachelor’s degree in Finance, Engineering, Statistics, or a closely related field (or foreign education equivalent) and three (3) years of experience as a Senior Quantitative Developer (or closely related occupation) developing and deploying quantitative models and analytical solutions (risk management, portfolio construction, performance analysis, or alpha research) within a financial services environment, using Python, Python data libraries, or SQL. Or, alternatively, Master’s degree in Finance, Engineering, Statistics, or a closely related field (or foreign education equivalent) and one (1) year of experience as a Senior Quantitative Developer (or closely related occupation) developing and deploying quantitative models and analytical solutions (risk management, portfolio construction, performance analysis, or alpha research) within a financial services environment, using Python, Python data libraries, or SQL. Skills and Knowledge : Candidate must also possess: Demonstrated Expertise (“DE”) supporting quantitative researchers, trading desks, or a risk management team by developing quantitative solutions, using Python, Shell, Git, and SQL; accessing database tables via SQL queries, using data from financial data vendors (Bloomberg, MorningStar, FactSet, or Barra) in financial calculations; and applying financial methodologies (Linear Regression, Time Series Analysis, or Monte-Carlo Simulation) in the development of applications, subsystems, services, or scripts, to support and enhance business decision analytics. DE supporting quantitative development using quantitative concepts and technical skills applicable to financial instruments (bonds, stocks, futures, or options); and categorizing financial instruments based on their characteristics to streamline risk management processes, using Python and SQL to develop risk management algorithms, automate data analysis, and generate comprehensive risk reports. DE developing quantitative models and analytical solutions, using Python (Numpy, Pandas, Statsmodels, and Matplotlib), Shell, Git, and SQL; developing REST API for stakeholders to interact with data analysis; and applying financial concepts (indices, risk exposure, stress test, or derivatives Greeks) when developing scripts to support and improve data-driven models. DE implementing the build and release of quantitative software applications, using version control tools (Bitbucket or Git), defect tracking tools (Jira), and Autosys to perform Continuous Integration/Continuous Deployment (CI/CD); performing coverage testing, troubleshooting, and debugging to validate data quality for quantitative productions, using Python IDE and SQL; and managing application versions and ensuring data quality in Prod and Sandbox environments.

Requirements

Bachelor’s degree in Finance, Engineering, Statistics, or a closely related field (or foreign education equivalent) and three (3) years of experience as a Senior Quantitative Developer (or closely related occupation) developing and deploying quantitative models and analytical solutions (risk management, portfolio construction, performance analysis, or alpha research) within a financial services environment, using Python, Python data libraries, or SQL.
Or, alternatively, Master’s degree in Finance, Engineering, Statistics, or a closely related field (or foreign education equivalent) and one (1) year of experience as a Senior Quantitative Developer (or closely related occupation) developing and deploying quantitative models and analytical solutions (risk management, portfolio construction, performance analysis, or alpha research) within a financial services environment, using Python, Python data libraries, or SQL.
Demonstrated Expertise (“DE”) supporting quantitative researchers, trading desks, or a risk management team by developing quantitative solutions, using Python, Shell, Git, and SQL; accessing database tables via SQL queries, using data from financial data vendors (Bloomberg, MorningStar, FactSet, or Barra) in financial calculations; and applying financial methodologies (Linear Regression, Time Series Analysis, or Monte-Carlo Simulation) in the development of applications, subsystems, services, or scripts, to support and enhance business decision analytics.
DE supporting quantitative development using quantitative concepts and technical skills applicable to financial instruments (bonds, stocks, futures, or options); and categorizing financial instruments based on their characteristics to streamline risk management processes, using Python and SQL to develop risk management algorithms, automate data analysis, and generate comprehensive risk reports.
DE developing quantitative models and analytical solutions, using Python (Numpy, Pandas, Statsmodels, and Matplotlib), Shell, Git, and SQL; developing REST API for stakeholders to interact with data analysis; and applying financial concepts (indices, risk exposure, stress test, or derivatives Greeks) when developing scripts to support and improve data-driven models.
DE implementing the build and release of quantitative software applications, using version control tools (Bitbucket or Git), defect tracking tools (Jira), and Autosys to perform Continuous Integration/Continuous Deployment (CI/CD); performing coverage testing, troubleshooting, and debugging to validate data quality for quantitative productions, using Python IDE and SQL; and managing application versions and ensuring data quality in Prod and Sandbox environments.

Responsibilities

Develops analytical solutions.
Develops and monitors portfolio personalization tool and efforts.
Enables users to run customized portfolio optimizations based on personal inputs.
Ensures all coding test cases are properly defined.
Reviews and ensures code format and quality satisfy the general engineering standard, and code documentations are sufficient.
Builds high quality, robust, and efficient analytical solutions to improve investment processes.
Analyzes and interprets statistical data to identify differences in relationships among sources of in-formation.
Evaluates technical data to determine effect on designs or plans.
Identifies relationships and trends in data to determine effects on research results.
Develops and implements complex techniques or analytics applications to transform raw data using data-oriented programming languages and visualization software.
Provides high level technical support to production applications to ensure their stability, performance, and reliability.