Manager, Quantitative Data Analytics and Insights

Fidelity Investments•Boston, MA

55d•$120,000 - $141,000•Hybrid

About The Position

Collects and analyzes data and develops decision support software, services, or products. Develops and supplies optimal time, cost, or logistics networks for program evaluation, review, or implementation. Evaluates the statistical methods and procedures used to obtain data to ensure validity, applicability, efficiency, and accuracy. Prepares data for processing by organizing information, checking for inaccuracies, and adjusting and weighting the raw data.

Requirements

Bachelor's degree in Computer Science, Engineering, Information Technology, Information Systems, Mathematical Finance and Financial Technology, or a closely related field (or foreign education equivalent) and three (3) years of experience as a Manager, Quantitative Data Analytics and Insights (or closely related occupation) performing quantitative data analysis to support investment research in a financial services industry by building interactive dynamic dashboard solutions, using Python, R, Snowflake, and Oracle within Windows and Linux environments.
Or, alternatively, Master's degree in Computer Science, Engineering, Information Technology, Information Systems, Mathematical Finance and Financial Technology, or a closely related field (or foreign education equivalent) and one (1) year of experience as a Manager, Quantitative Data Analytics and Insights (or closely related occupation) performing quantitative data analysis to support investment research in a financial services industry by building interactive dynamic dashboard solutions, using Python, R, Snowflake, and Oracle within Windows and Linux environments.
Demonstrated Expertise ("DE") performing fixed income risk analysis using BarraOne to identify key risk drivers; developing Python-based framework for performance attribution, risk analysis, and reporting automation; and perform critical analysis using Git version control and financial tools (Bloomberg and FactSet).
DE designing, building, and maintaining robust and scalable data pipelines, using Python, smtplib, win32com, and SQLAlchemy; integrating disparate data sources (Oracle and Snowflake) using advanced SQL and Python; and implementing data validation and automated processes to ensure data integrity and consistency, and guaranteeing high-quality data for analysis, using automated Python script with task scheduler in Autosys framework.
DE translating complex business problems into analytical frameworks, defining Key Performance Indicators (KPIs), and employing robust methodologies to deliver actionable insights, using Tableau, PowerPoints, and Power BI; and responding to ad-hoc requests to optimize workflows for efficiency and scalability, using Confluence and Jira.
DE building quantitative tools that deliver actionable insights into fund performance attribution, risk contribution, and portfolio construction, using Python Pandas, NumPy, and CVXPY; and transforming and analyzing complex datasets and developing interactive data visualization tools that empower stakeholders to make data-driven investment decisions, using Python Dash and Plotly.

Responsibilities

Formulates and applies mathematical modeling and other optimizing methods to developing and interpreting information that assists management with decision making, policy formulation, or other managerial functions.
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.
Interacts with senior leadership and collaborates with colleagues across the enterprise.
Collates data from multiple sources to create comprehensive fund performance monitoring tools.
Performs historical data analysis to detect and address systemic issues and strengthen analytic models that provide an accurate risk profile of investment decision process.
Collaborates with technology teams to implement enterprise level solutions.

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