Lead Data Science Manager - Global Payments & ML

Capital One National Association; New York (NY); On-site; USD 251,000 - 286,000; Full time

Job summary

Capital One is seeking a Senior Manager, Data Science - Global Payment Network in New York, NY. This leadership role guides a team delivering ML models across fraud/risk and merchant analytics, leveraging vast datasets and a modern tech stack.

You will partner with software engineers, product managers, and data scientists to ship products customers love, applying open-source tools and cloud platforms to turn data into business value.

Qualifications

• Bachelor's degree in a quantitative field plus 11 years of analytics experience
• Master's degree in a quantitative field or MBA with quantitative concentration plus 9 years analytics
• PhD in a quantitative field plus 6 years analytics
• 6+ years of experience using open source languages for large-scale data analysis
• 6+ years of experience with machine learning
• 6+ years of experience using relational databases

Responsibilities

• Lead a group of data scientists, business analysts and data analysts to develop ML models from design through deployment
• Partner with cross-functional team to deliver a product customers love
• Leverage Python, Conda, AWS, H2O, Spark to extract insights from large datasets
• Translate complex work into tangible business goals

Skills

Leadership

Cross-functional collaboration

Open-source languages

Cloud computing

Interpersonal skills

Data science

Education

Bachelor's degree in quantitative field

Master's degree in quantitative field or MBA

PhD in quantitative field

Tools

Python

Conda

AWS

H2O

Spark

Job description

Capital One is seeking a Senior Manager, Data Science - Global Payment Network in New York, NY. This leadership role guides a team delivering ML models across fraud/risk and merchant analytics, leveraging vast datasets and a modern tech stack.

You will partner with software engineers, product managers, and data scientists to ship products customers love, applying open-source tools and cloud platforms to turn data into business value.