Senior Data Analyst

Department: Real Estate – Quantitative Research
Supervisor: Vice President, Head of Quantitative Research
Location: Cleveland, OH
Employment Type: Regular Full-Time, Exempt

Position Overview

The Senior Data Analyst plays a key role within the Real Estate Quantitative Research team, supporting multiple business units through advanced data management, analytics, and process automation. This position is responsible for gathering, organizing, and transforming data from diverse sources to support real estate and fixed income market analysis. The role also drives automation initiatives for performance reporting and DDQ processes, ensuring accuracy, efficiency, and timely delivery. Additionally, the AVP contributes analytical insights for new product development, existing product performance evaluation, and asset allocation research.

Key Responsibilities

1. Data Analytics & Research

• Develop processes to convert raw data series and factor inputs from multiple sources into a structured, centralized system accessible across the investment platform.
• Build a deep understanding of performance and risk modeling, portfolio construction, factor calculations, and interpretation of quantitative output.
• Explore and evaluate alternative data sources and innovative modeling techniques to address investment challenges.
• Support senior team members in research related to macroeconomics, financial markets, and asset behavior across equities, fixed income, credit, and other asset classes.
• Design and maintain VBA macro models to automate reporting and analytical workflows for Fixed Income, Real Estate, and Research teams.
• Conduct industry analysis, quantitative research, and back-testing across various asset classes and investment products.
• Lead efforts to automate performance reporting and DDQ processes to improve accuracy and operational efficiency.

2. Product Development, Performance Analysis & Asset Allocation

• Assist with monthly and quarterly BondEdge performance reporting, including interest rate scenario analysis, spread change analysis, and key rate duration assessments.
• Create and maintain templates, tools, and mechanisms used for BWAM Optimizer analysis.
• Support the development and maintenance of internal and external materials for Multi-Sector investment strategies.
• Perform high-level analysis using Morningstar Direct and eVestment, including asset allocation studies, peer group tracking, and competitive benchmarking.

Qualifications

• Strong analytical and quantitative skills with experience in financial modeling and data transformation.
• Proficiency in Excel, VBA, and analytical tools commonly used in investment research.
• Familiarity with fixed income analytics platforms (e.g., BondEdge) and investment databases (e.g., Morningstar Direct, eVestment) preferred.
• Ability to work collaboratively across teams and communicate complex concepts clearly.
• Experience in real estate, fixed income, or multi-asset research is a plus.

Employment Classification

• Regular Full-Time, Exempt
  (Other classifications such as Temporary, Part-Time, Nonexempt, or Intern may be used for alternate hiring tracks.)

Education, Skills, Personal Characteristics, and Experience Requirements

• Bachelor’s degree in Finance, Mathematics, Computer Science, or a related quantitative discipline from a respected university.
• Advanced degree (Master’s or PhD) in Finance, Mathematics, Computer Science, or a related field is strongly preferred.
• Minimum of 6 months of experience in quantitative research or a specialized area such as financial engineering, data science, or risk analytics within the securities industry.
• Strong foundation in machine learning, hypothesis testing, regression analysis, statistics, and probability, with demonstrated experience building predictive analytics on noisy or complex datasets.
• Knowledge of financial engineering concepts and the ability to analyze portfolios across a broad range of financial instruments, including equities, fixed income, currencies, and derivatives.
• Proficiency in statistical programming languages such as Python, R, and SQL, with the ability to manipulate large datasets and extract meaningful insights.
• Excellent written and verbal communication skills, with the ability to present analytical findings and recommendations clearly, concisely, and in a timely manner.
• Demonstrated interest in investment management, capital markets, and long‑term strategic investing.