Senior Data Scientist, Real-Time Fraud & Protection

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

Benefits offered by this job

Health benefits

Competitive compensation

Job summary

Capital One is hiring a Principal Associate Data Scientist in McLean, VA to advance debit transaction fraud detection. You will work with data scientists, engineers, and product managers to build real-time models using Python, SQL and AWS, spanning from design to production deployment, and translate findings into business impact.

You will develop and validate models, leverage gradient boosting and advanced ML techniques, and contribute to scalable data science workflows in a fast-paced

Qualifications

• Bachelor's degree in a quantitative field with 5 years analytics experience.
• Master's degree (or MBA with quantitative focus) with 3 years analytics experience.
• PhD in a quantitative field may be substituted for work experience.

Responsibilities

• Partner with cross-functional teams to deliver fraud-prevention products.
• Utilize Python, AWS, SQL and big data tools to derive insights from large datasets.
• Develop ML models across design, training, evaluation and deployment.
• Translate complex modeling results into actionable business goals.

Skills

Cross-functional collaboration

Analytical mindset

Strategic thinking

Business communication

Education

Bachelor's degree in quantitative field

Master's degree in quantitative field or MBA with quantitative concentration

PhD in quantitative field

Tools

Python

Conda

AWS

H2O

Spark

SQL

pytest

mypy

linting

CI/pre-commit

Job description

Capital One is hiring a Principal Associate Data Scientist in McLean, VA to advance debit transaction fraud detection. You will work with data scientists, engineers, and product managers to build real-time models using Python, SQL and AWS, spanning from design to production deployment, and translate findings into business impact.

You will develop and validate models, leverage gradient boosting and advanced ML techniques, and contribute to scalable data science workflows in a fast-paced