Capital One Recruitment Program 2025 | Hiring Senior Associate, Business Analysis

Qualifications

Basic Qualifications:

• Degree in statistics, math, engineering, economics, econometrics, financial engineering, finance, or operations research with a quantitative emphasis preferred
• Atleast 2 years relevant work experience
• Experience in Python or R

Preferred Skills:

• Proficiency in key econometric and statistical techniques (such as predictive modeling, logistic regression, panel data models, decision trees, machine learning methods)
• Atleast 2 years of experience model development or validation
• Atleast 2 years of experience in R or Python for large scale data analysis
• Atleast 2 years of experience with relational databases and SQL
• Strong analytical skills with high attention to detail and accuracy
• Excellent written and verbal communication skills

The ideal candidate is:

• Inquisitive. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You’re not afraid to share a new idea.
• Technical. You’re comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms.
• Statistically-minded. You’ve built models, validated them, and backtested them. You know how to interpret a confusion matrix or a ROC curve. You have experience with clustering, classification, time series, and deep learning.
• Innovative. You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them.

Role Description:

In this role you will:

• Partner cross-functionally with data scientists, quantitative analysts, business analysts, software engineers, and project managers to manage the risk and uncertainty inherent in statistical and machine learning models in order to lead Capital One to the best decisions, not just avoid the worst ones.
• Build and validate statistical and machine learning models through all phases of development, from design through training, evaluation and implementation
• Develop new ways of identifying weak spots in model predictions earlier and with more confidence than the best available methods
• Assess, challenge, and at times defend state-of-the-art decision-making systems to internal and regulatory partners
• Leverage a broad stack of technologies — Python, R, Conda, AWS, and more — to reveal the insights hidden within huge volumes of data
• Build upon your existing machine learning and statistical toolset – both by learning new technologies and by building custom software tools for data exploration, model performance evaluation, and more
• Communicate technical subject matter clearly and concisely to individuals from various backgrounds both verbally and through written communication; prepare presentations of complex technical concepts and research results to non-specialist audiences and senior management
• Flex your interpersonal skills to translate the complexity of your work into tangible business goals, and challenge model developers to advance their modeling, data, and analytic capabilities