Principal Data Scientist - AI Foundations, Specialist Models

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

The Principal Data Scientist will join the AI for Data team to build and ship machine learning solutions that support the enterprise data lifecycle. This role involves end-to-end model development, cross-team collaboration, and translating technical work into business goals.

Location

McLean, VA (onsite)

Compensation

USD 161,800 - 184,600 per year

Role Summary

This Principal Data Scientist position on the AI for Data team focuses on delivering state-of-the-art machine learning solutions. Work includes partnering across teams, developing machine learning models from design through implementation, and aligning technical complexity to enterprise business objectives.

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

Team Overview

• The AI for Data team builds and ships state-of-the-art machine learning solutions to support data lifecycle across the enterprise
• Partners with product, tech, and design teams to deliver personalized experiences for data and platform users, with the goal of driving productivity and innovation
• Leads experimentation and innovation to create next generation experiences using emerging ML technologies

Technology Stack

• Python, Conda, AWS, H2O, Spark, Scala, R, SQL

Minimum Qualifications

• Minimum experience: 5 years
• Degree and experience options (with the expectation the required degree will be obtained on or before the scheduled start date):
  • A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 5 years of experience performing data analytics
  • A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 3 years of experience performing data analytics
  • A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field)

Preferred Qualifications

• Master's Degree in a "STEM" field (Science, Technology, Engineering, or Mathematics), or PhD in a "STEM" field
• Experience working with AWS
• At least 3 years' experience in Python, Scala, or R
• At least 3 years' experience with machine learning
• At least 3 years' experience with SQL

Additional Competency Profile

• Innovative: Continually evaluates emerging technologies and seeks opportunities to apply state-of-the-art methods
• Creative: Works on defining solutions for big, undefined problems and shares new ideas
• Technical: Uses open-source languages and has hands-on experience building data science solutions with open-source tools and cloud computing platforms
• Statistically-minded: Builds, validates, and back-tests models; interprets outputs such as a confusion matrix or ROC curve; has experience with clustering, classification, sentiment analysis, time series, and deep learning
• Data-focused: Retrieves, combines, and analyzes data from varied sources and structures

Other Compensation and Incentives

• Eligible for performance-based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
• Incentives may be discretionary or non-discretionary depending on the plan

Application Timeline and Statements

• Expected to accept applications for a minimum of 5 business days
• No agencies please.
• Capital One is an equal opportunity employer (EOE), including disability and veteran status, committed to non-discrimination
• Capital One promotes a drug-free workplace
• Capital One may consider sponsoring a new qualified applicant for employment authorization for this position
• For technical support or questions about the recruiting process: [e-mail]
• Capital One does not provide, endorse, or guarantee third-party products, services, educational tools, or other information on this site