Senior Quantitative Developer

Fidelity Investments•Boston, MA

9h•Hybrid

About The Position

Position Description: Develops analytical solutions and performs alpha research using probability, linear regression, and time series data analysis. Supports investment needs and develops new solutions using analytical and quantitative concepts. Adds scale, rigor, and repeatability to quantitative research using full-stack software development methodologies. Executes programming deliverables using R, Java, SQL, and Linux. Participates in full stack development projects on the frontend (User Interfaces (UIs)) and backend (Application Programming Interfaces (APIs)), using Python, R, and SQL. Develops new models and products to enable organizational advantages in the marketplace, using statistics, econometrics, and quantitative techniques or methods.

Requirements

Bachelor’s degree in Quantitative and Computational Finance, Computer Science, Accounting, Financial Mathematics, Actuarial Science, 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 within a financial services environment, using Python, R, or SQL.
Or, alternatively, Master’s degree in Quantitative and Computational Finance, Computer Science, Accounting, Financial Mathematics, Actuarial Science, 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 within a financial services environment, using Python, R, or SQL.
Demonstrated Expertise (“DE”) developing analytical solutions using DBeaver, JupyterLab, and Tableau
DE developing models for multi-asset class products using Python, R, or SQL
DE implementing and deploying portfolio construction methodologies using Python or R
DE developing applications using statistical models, quantitative methods, Machine Learning (ML), and optimization tools (CVXPY, Axioma, or CPLEX).
DE developing enterprise applications using SQL, Python (NumPy, Pandas, SciPy, Statsmodels, Scikit-learn, Matplotlib, and Plotly), or R (Dplyr, Lubridate, Tidyverse, and Ggplot2)
DE developing, testing, and deploying production systems using Continuous Integration and Continuous Deployment (CI/CD) protocols
DE building Web applications using Python or R
DE implementing best practices in using version control (Git, GitHub, Gitlab, or Bitbucket).
DE developing and implementing working quantitative models with research and technology teams, using Python, R, or SQL
DE extracting data from vendors -- Bloomberg, DataStream, Morningstar, FactSet, or Concord
DE designing and optimizing pipelines with ActiveBatch, AutoSys (JIL), or Airflow (YAML) to process data in Snowflake, NoSQL, and Oracle SQL or SQL Server
DE constructing and optimizing multi-step processes with Amazon Web Services (AWS) resources.
DE completing the software development lifecycle (SDLC) using OS, I/O, and network (MobaXterm/PuTTY)
DE developing solutions in VS Code, PyCharm, and Docker
DE managing application versions and ensuring data quality in Prod, User Acceptance Testing (UAT), and Sandbox
DE performing Application Lifecycle Management (ALM), package management (using JFrog Artifactory), configuration management, and implementing webhooks.

Responsibilities

Develops analytical solutions.
Develops and monitors portfolio personalization tools 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 documentation 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 information.
Evaluates technical data to determine the effect on designs or plans.
Identifies relationships and trends in data to determine effects on research results.
Develops and implements a set of techniques or analytics applications to transform raw data into meaningful information using data-oriented programming languages and visualization software.
Applies data mining, data modeling, natural language processing, and machine learning to extract and analyze information from large structured and unstructured datasets.