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Fidelity Bank · 6 hours ago

Manager, Quantitative Data Analytics and Insights

Boston, MA

Full-time

Hybrid

Mid Level

$131K/yr - $141K/yr

3+ years exp

Fidelity Bank is a leading financial institution, and they are seeking a Manager for Quantitative Data Analytics and Insights. The role involves expanding and improving technical and data infrastructure, providing analytic solutions, and ensuring quality assurance for investment intelligence.

BankingBitcoinCustomer ServiceFinancial ServicesInsuranceWealth Management

Responsibilities

Coordinates production data management, model integrity, and end-to-end technical support for portfolio management, risk models, and quant research platforms

Provides analytic solutions and reporting frameworks to partners and senior leaders across the organization

Delivers innovative data visualization and analytic tools capable of illustrating point-in-time and time series investment themes, portfolio exposures, and factors driving fund performance

Provides oversight, maintenance, quality assurance, and operational support of analytic and reporting environments to ensure timely and accurate investment intelligence is delivered for direct use within the portfolio management process

Oversees daily, weekly, and monthly production reporting cycles across the analytic environments

Ensures all required input data is available, processes run successfully, statistical output is accurate, and reports are generated properly

Responds to ad-hoc data analysis requests in support of projects performed by equity, fixed income or multi-asset class Quant Analysts

Develops new processes, performs calculations, identifies anomalies, and produces new reporting capabilities

Contributes to complex projects within a fast-paced environment

Liaises with investment professionals to gather requirements and achieve deliverables of technical development teams

Qualification

Advanced data analysisPythonFixed income productsR-ShinyData visualizationTeam coordinationProblem solvingCommunication skills

Required

Bachelor's degree in Computer Science, Engineering, Information Technology, Information Systems, Financial Mathematics, 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 investment research in a fixed income investment domain

Or, alternatively, Master's degree in Computer Science, Engineering, Information Technology, Information Systems, Financial Mathematics, 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 investment research in a fixed income investment domain

Demonstrated Expertise (“DE”) performing advanced data analysis for security-level analytics and fixed income products, using Bloomberg data (price, yield, Option-Adjusted Spread (OAS), Option-Adjusted Duration (OAD), and valuation); and performing data validation and model enhancements, and providing advisory services for fixed income market data operations, using python packages (Pandas, SciPy, and Matplotlib)

DE performing stress test simulation for money market funds (asset backed secured and unsecured bonds) in accordance with Rule 2a-7, using Python (XLWings or OpenPyXL) for data interaction and Excel VBA to generate NAV results under various redemption and liquidity scenarios

DE developing performance attribution factor analysis tools for Fixed Income Funds, using R-Shiny, Python-Flask, or Django (as the back-end) and Dash-Plotly, Angular, or Reach (as the front-end); developing Application Programming Interfaces (APIs) for seamless analytics data retrieval from platforms (Bloomberg); and creating interactive web dashboards to present asset performance insights, using R-Shiny, Python-Flask, or Django

DE developing index rebalancing and validation (daily and monthly) processes according to methodology rules; developing factor risk models for funds to analyze risk and inform sector allocation, using Statsmodels python package and SciKit-learn; and generating client-facing reports and incorporating what-if scenario analysis (to support investment strategies)