Quantitative Analyst

Quantitative Analyst – Real Estate Credit

Company Description

Bluebono is a real estate credit platform focused on specialized lending, private credit, and data-driven investment and underwriting decisions.

We work across real estate lending, credit analysis, portfolio management, operations, and capital markets, with an increasing focus on using data, quantitative modeling, and technology to improve how decisions are made.

Role Description

Bluebono is seeking a Quantitative Analyst to help build the data, modeling, and analytical capabilities supporting our business.

The role is designed for someone who enjoys working with data and using quantitative methods to solve real-world problems. The analyst will work with financial, real estate, loan, operational, and market data to identify patterns, develop models, test assumptions, and build tools that support better business decisions.

Real estate credit will be the primary area of application, but the role is not limited to traditional lending analysis. Projects may involve credit risk, pricing, property analysis, portfolio performance, capital allocation, operational efficiency, forecasting, or other business problems where quantitative analysis can create value.

We are particularly interested in candidates with a background in financial engineering, quantitative finance, applied mathematics, statistics, data science, or another highly quantitative discipline.

More importantly, we are looking for someone who is genuinely curious, enjoys working with data, and has a strong interest in applying analytical thinking to practical problems .

Key Responsibilities

Data Analysis and Infrastructure

• Collect, organize, clean, and connect data from different business systems and external sources.
• Build structured datasets that can support financial analysis, statistical modeling, and predictive analytics.
• Develop consistent data definitions, calculations, and validation processes.
• Identify data-quality issues, missing information, and inconsistencies that may affect analysis or model results.
• Help improve the way data is collected, stored, and used across the organization.

Quantitative and Predictive Modeling

• Develop analytical and predictive models using financial, property, borrower, transaction, market, and historical performance data.
• Apply statistical and machine-learning techniques to identify relationships and predict future outcomes.
• Perform feature engineering, model testing, validation, and performance analysis.
• Compare model predictions with actual results and continuously refine assumptions and methodologies.
• Explore modeling applications such as credit risk, loan performance, pricing, property value, payoff timing, delinquency, liquidity, and portfolio behavior.
• Evaluate when a simple analytical approach is more appropriate than a complex model and communicate those trade-offs clearly.

Real Estate Credit Analytics

• Analyze loan, borrower, property, and market characteristics that may affect credit performance and investment outcomes.
• Evaluate metrics such as LTV, LTC, DSCR, leverage, loan duration, pricing, yield, concentration, and profitability.
• Analyze historical loan performance to identify trends, risk factors, and opportunities.
• Support scenario analysis and stress testing based on changes in interest rates, property values, credit conditions, and market liquidity.
• Help develop quantitative approaches to underwriting, pricing, portfolio monitoring, and credit-risk evaluation.

Business and Portfolio Analysis

• Analyze performance across products, markets, channels, and portfolios.
• Develop models to evaluate profitability, capital utilization, funding costs, and expected returns.
• Identify trends and relationships that may improve business strategy or operational performance.
• Build reporting and analytical tools that allow management to better understand business and portfolio performance.
• Support ad hoc quantitative analysis for new products, business opportunities, and strategic initiatives.

Tools and Automation

• Build analytical tools and automated workflows using Python, SQL, Excel, and other appropriate technologies.
• Reduce repetitive manual analysis by developing reusable models and automated processes.
• Create dashboards or reporting tools where appropriate.
• Document models, assumptions, methodologies, and data sources so analyses can be reviewed and reproduced.
• Work with business and technology teams to develop scalable analytical solutions.

Qualifications

• Bachelor’s or master’s degree in Financial Engineering, Quantitative Finance, Applied Mathematics, Statistics, Data Science, Computer Science, Engineering, Economics, Finance, or a related quantitative field.
• 2+ years of experience in quantitative analysis, data analytics, financial modeling, data science, or a related role.
• Strong understanding of statistics, probability, regression, and quantitative analysis.
• Experience working with real-world datasets, including data cleaning, validation, and integration from multiple sources.
• Experience developing analytical, statistical, or predictive models.
• Strong problem-solving skills and ability to approach unfamiliar problems systematically.
• Ability to communicate quantitative findings clearly to both technical and nontechnical audiences.
• Strong attention to detail, intellectual curiosity, and willingness to independently investigate problems.

What We Value

We are looking for someone who is genuinely passionate about data, modeling, and problem solving .

The ideal candidate does more than produce reports. They ask questions, investigate patterns, test assumptions, and look for better ways to understand a problem.

We value someone who can take an imperfect real-world dataset, determine what is useful, build a reasonable analytical framework, and translate the result into something the business can actually use.

What Success Looks Like

Success in this role means helping Bluebono develop a stronger quantitative foundation for its real estate credit platform.

Over time, the analyst should help transform raw business and financial data into reliable datasets, reusable models, predictive tools, and practical insights that improve decisions across credit, pricing, portfolio management, capital allocation, and business operations.