Principal Data Scientist - Enterprise ML & Data Solutions

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

Benefits offered by this job

Performance-based incentive

Equal opportunity employer

Job summary

Capital One is seeking a Principal Data Scientist to join the AI for Data team in McLean, VA, onsite. You will develop state-of-the-art machine learning solutions spanning design to implementation, aligning technical work with enterprise business goals.

You will partner with product, tech, and design teams to deliver data-driven experiences, build models for large datasets, and translate complex analytics into business impact. Strong collaboration and leadership are essential.

Qualifications

• Minimum 5 years of data analytics experience.
• Bachelor's Degree +5 years data analytics.
• Master's Degree +3 years data analytics or MBA with quantitative concentration.
• PhD in a quantitative field.

Responsibilities

• Collaborate with a cross-functional team of data scientists, software engineers, and product managers to deliver customer-facing products.
• Apply a broad technology stack, including Python, Conda, AWS, H2O, Spark, and more, to extract insights from large volumes of numeric and textual data.
• Develop machine learning models across the full lifecycle, including design, training, evaluation, validation, and implementation.
• Use interpersonal skills to translate model complexity into tangible business goals.

Skills

Python

Conda

AWS

H2O

Spark

Scala

R

SQL

Machine learning

Education

Bachelor's Degree in a quantitative field

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

PhD in a quantitative field

Tools

Conda

AWS

H2O

Spark

Scala

R

Job description

Capital One is seeking a Principal Data Scientist to join the AI for Data team in McLean, VA, onsite. You will develop state-of-the-art machine learning solutions spanning design to implementation, aligning technical work with enterprise business goals.

You will partner with product, tech, and design teams to deliver data-driven experiences, build models for large datasets, and translate complex analytics into business impact. Strong collaboration and leadership are essential.