Lead Data Science Manager - Hybrid, Marketing

ITSMF; McLean (VA); Hybrid; USD 197,000 - 225,000; Full time

Job summary

Capital One is seeking a Manager, Data Science - US Card - Marketing to lead data-driven marketing decisions using machine learning and predictive modeling. You will work with Python, Spark, AWS, and open-source tools to develop models that inform campaigns and customer experiences across large data sets.

The role requires collaboration with engineers, analysts, and product managers, and operates in a hybrid office setting with on-site presence as required by policy.

Qualifications

• Bachelor's degree in a quantitative field plus 6 years analytics
• Master's degree in a quantitative field or MBA with quantitative concentration plus 4 years analytics
• PhD in a quantitative field plus 1 year analytics experience
• At least 1 year of experience leveraging open source programming languages for large scale data analysis
• At least 1 year of experience working with machine learning
• At least 1 year of experience utilizing relational databases

Responsibilities

• Partner with a cross-functional team to deliver a product customers love
• Leverage a broad stack of technologies - Python, Conda, AWS, H2O, Spark
• Build machine learning models through all phases of development from design to implementation
• Translate complex work into tangible business goals
• Adhere to hybrid on-site presence policy at designated office location

Skills

Open source programming

Machine learning

Relational databases

Data analytics

Education

Bachelors degree in quantitative field

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

PhD in quantitative field

Tools

Python

Scala

R

SQL

Spark

AWS

Job description

Capital One is seeking a Manager, Data Science - US Card - Marketing to lead data-driven marketing decisions using machine learning and predictive modeling. You will work with Python, Spark, AWS, and open-source tools to develop models that inform campaigns and customer experiences across large data sets.

The role requires collaboration with engineers, analysts, and product managers, and operates in a hybrid office setting with on-site presence as required by policy.