Senior Quantitative Analyst - Global Finance & ML

capitalone; McLean (VA); On-site; USD 162,000 - 185,000; Full time

Job summary

Capital One is seeking a Principal Quantitative Analyst in Global Finance to develop and implement advanced quantitative models for capital markets and finance applications. You will partner with business lines, build cloud-based analytics, and drive data-driven insights across billions of records.

The role requires deep expertise in statistics, machine learning, and large data analysis, with a track record of presenting complex results to leadership and non-technical stakeholders.

Qualifications

• Master’s degree or PhD in a quantitative field (Statistics, Economics, OR, Analytics, Math, CS) or MBA with quantitative concentration plus 3 years of experience.
• 3+ years in statistical/econometric modeling, and in regression (linear and logistic).
• 3+ years programming in R, Python or SQL and presenting results to non-statistical audiences.

Responsibilities

• Partner with lines of business to develop and enhance Capital Markets modeling and analytic frameworks (derivatives and fixed income).
• Create novel analytical solutions across Capital One entities to solve business problems.
• Apply quantitative methods and automation to improve performance and efficiency.
• Collaborate in a cross-disciplinary team to build cloud-based data-driven solutions.
• Communicate technical concepts clearly to diverse audiences and support leadership with insights.

Skills

Statistical modeling

Linear regression

Logistic regression

Machine learning

Data analysis

Large datasets

Presenting results

Education

Master's degree in quantitative field

PhD in quantitative field

Tools

Python

R

SQL

Job description

Capital One is seeking a Principal Quantitative Analyst in Global Finance to develop and implement advanced quantitative models for capital markets and finance applications. You will partner with business lines, build cloud-based analytics, and drive data-driven insights across billions of records.

The role requires deep expertise in statistics, machine learning, and large data analysis, with a track record of presenting complex results to leadership and non-technical stakeholders.