Principal Associate, Data Science - Small Business Bank (Fraud) at Capital One

Location: Center 2 (19050), United States of America, McLean, Virginia

About the Role

Capital One is at the forefront of innovation in the financial services sector, leveraging data to drive decision-making. As a Principal Associate, Data Science in our Small Business Bank (Fraud) division, you will be integral in our mission to protect our customers and Capital One from fraudulent activities. This role is pivotal in utilizing state-of-the-art statistical modeling and machine learning technologies to deliver insights from billions of customer records, helping individuals and businesses thrive financially.

Team Description

In Small Business Banking (SBB), we prioritize understanding the needs of our small business owners. The SBB Data Science team is dedicated to advancing data science through statistics, machine learning, and emerging domains. We create innovative solutions to help our partners design and deliver products and policies that exceed small businesses’ expectations in financial services. Collaborating with cross-functional teams, including business analysts, product managers, and engineers, we identify and enable intelligent decisions to counteract fraud.

Key Responsibilities

• Collaborate with a cross-functional team of data scientists, business analysts, software engineers, and product managers to deliver customer-centric products.
• Build and develop machine learning models throughout all phases, from design to implementation.
• Utilize a broad technology stack including Python, Conda, AWS, H2O, and Spark to analyze vast amounts of numeric and textual data.
• Translate complex analytical work into actionable business goals through your strong interpersonal skills.

Ideal Candidate Profile

The ideal candidate will possess:

• Statistical Acumen: Experience with model development, validation, backtesting, and understanding of clustering, classification, sentiment analysis, time series, and deep learning.
• Technical Expertise: Proficiency in open-source languages with a passion for further development and experience in data science solutions using cloud computing platforms.
• Data Mastery: Skill in retrieving, combining, and analyzing data from various sources and structures.
• Innovative Thinking: Continuous research and evaluation of emerging technologies with application to real-world scenarios.
• Customer Focus: A commitment to making the right decisions for our customers, balancing analysis with pragmatic action.

Basic Qualifications

• Bachelor’s Degree plus 5 years of experience in data analytics, or
• Master’s Degree plus 3 years in data analytics, or
• PhD with an expectation that the required degree will be obtained on or before the scheduled start date.
• At least 1 year of experience in open-source programming languages for large-scale data analysis.
• At least 1 year of experience with machine learning.
• At least 1 year of experience with relational databases.

Preferred Qualifications

• Master’s or PhD in a STEM field (Science, Technology, Engineering, or Mathematics).
• At least 3 years’ experience with machine learning.
• At least 1 year of experience with deep learning/neural networks.
• At least 1 year of experience working with AWS.
• At least 3 years’ experience with Python, Dask, Scala, or R.
• At least 3 years’ experience with SQL.

Capital One is an equal opportunity employer committed to diversity in the workplace. All qualified applicants will receive consideration for employment without regard to sex, race, color, age, national origin, religion, disability, genetic information, marital status, sexual orientation, gender identity, citizenship, immigration status, veteran status, or any other basis protected by applicable federal, state, or local law.

If you require an accommodation for the application process, please contact Capital One Recruiting.

Capital One offers comprehensive health, financial, and other benefits that support your total well-being. Learn more by visiting the . Eligibility varies based on employment status and management