Senior Data Science Manager, Model Risk Leader

Capital One; McLean (VA); Presencial; USD 230.000 - 262.000; Jornada completa

Descripción de la vacante

Capital One in McLean, VA seeks a Senior Manager, Data Science - Model Risk to lead advanced analytics and ML initiatives within the Model Risk Office. You will guide a team to defend against model failures while delivering insights from billions of records.

Role emphasizes leadership, customer-centric decision making, and applying state-of-the-art ML methods with a broad tech stack including Python, AWS, Spark, and H2O. Strong collaboration across risk, product, and engineering is essential.

Formación

• Bachelor's/Master's/PhD in a quantitative field with required years of experience or equivalent.
• 2+ years of experience leveraging open source programming languages for large-scale data analytics.
• 2+ years of experience working with machine learning.
• 2+ years of experience utilizing relational databases.

Responsabilidades

• Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love.
• Leverage a broad stack of technologies — Python, Conda, AWS, H2O, Spark, and more — to reveal insights from large volumes of data.
• Build machine learning models through all phases of development: design, training, evaluation, validation, and implementation.
• Translate complex work into tangible business goals through strong communication.

Conocimientos

Machine learning

Data analytics

Leadership

Communication

Team collaboration

Problem solving

Open source programming languages

Educación

Bachelor's Degree in quantitative field

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

PhD in quantitative field

Herramientas

Python

Scala

R

AWS

Spark

H2O

Descripción del empleo

Capital One in McLean, VA seeks a Senior Manager, Data Science - Model Risk to lead advanced analytics and ML initiatives within the Model Risk Office. You will guide a team to defend against model failures while delivering insights from billions of records.

Role emphasizes leadership, customer-centric decision making, and applying state-of-the-art ML methods with a broad tech stack including Python, AWS, Spark, and H2O. Strong collaboration across risk, product, and engineering is essential.