Manager, Quant Data Analytics and Insights

Fidelity Investments•Boston, MA

6d•$126,000 - $141,000•Onsite

About The Position

Develops and maintains technical infrastructure to support quantitative research and investment processes across fixed income and equity Environmental, Social, and Governance (ESG) models. Implements robust model validation frameworks, builds scalable data pipelines, and delivers advanced analytics and visualization tools. Maintains version control and CI/CD workflows using GitHub and Jira. Schedules production jobs using Autosys. Contributes to the integration of non-traditional and unstructured data sources and applies statistical and time-series techniques to ensure model accuracy and robustness. Supports quantitative research initiatives through tooling, automation, and governance enhancements.

Requirements

Bachelor’s degree in Computer Science, Engineering, Quantitative Economics, Mathematics, Mathematical Finance, Financial Technology, or a closely related field (or foreign education equivalent) and three (3) years of experience as a Manager, Quant Data Analytics and Insights (or closely related occupation) performing data engineering and quantitative model deployment by building and validating end-to-end ESG solutions using Python, Snowflake, Oracle, GitHub, and FactSet in a financial services industry.
Master’s degree in Computer Science, Engineering, Quantitative Economics, Mathematics, Mathematical Finance, Financial Technology, or a closely related field (or foreign education equivalent) and one (1) year of experience as a Manager, Quant Data Analytics and Insights (or closely related occupation) performing data engineering and quantitative model deployment by building and validating end-to-end ESG solutions using Python, Snowflake, Oracle, GitHub, and FactSet in a financial services industry.
Demonstrated Expertise (“DE”) performing quantitative ESG model validation by applying ESG scoring methodology and equity/fixed income factor using Python and SQL;
DE performing back-testing and sensitivity analysis to implement algorithmic improvements and enhance model robustness using Python, SQL, Snowflake, and Oracle;
DE performing model development lifecycle support and CI/CD workflow maintenance using GitHub and Jira.
DE performing data extraction and integration for quantitative ESG models by assessing, processing, and documenting data relationships, definitions, and schema structures across relational and cloud-based data environments using SQL, Snowflake, Oracle, Python, and FactSet;
DE processing and integrating API outputs and vendor feeds from sources including Morgan Stanley Capital International (MSCI) to deliver clean, model-ready datasets using Python and SQL.
DE designing and developing interactive analytical dashboards and data visualization solutions for ESG models to evaluate model performance and support portfolio construction decisions, using Python, Streamlit, Plotly, Matplotlib, and Seaborn.
DE designing and deploying a systematic framework for integrity testing across Snowflake and Oracle environments using Python, SQL, Excel, and VBA;
DE applying advanced analytics to validate historical data accuracy for quantitative modeling and production using Python, and SQL, including performing outlier detection and time-series analysis.

Responsibilities

Validates complex quantitative ESG models for fixed income and equity portfolios by systematically verifying research code logic.
Performs sustainable investing research including model construction, factor definitions, factor calculations, and translates output statistics into meaningful information.
Designs and develops interactive dashboards for ESG model performance and portfolio analytics.
Performs schema mapping and onboarding of multi-asset ESG datasets to validate alignment with quantitative model requirements and ensure consistency across diverse data sources.
Processes and integrates raw vendor feeds and Application Programming Interface (API) outputs for ESG ratings, sustainable investment strategies, market data, and factor exposures, delivering standardized, model-ready datasets optimized for downstream analytics and portfolio construction.
Responds to ad-hoc requests for data analysis, back-testing, and visualization in support of quantitative research projects.
Performs daily, weekly, and monthly production reporting cycles across analytic environments.
Ensures all required input data is available, processes run successfully, statistical output is accurate, and reports are generated properly.
Reports results of statistical analyses, including information in the form of graphs, charts, and tables.
Determines whether statistical methods are appropriate, based on user needs or research questions of interest.