Principal Data Scientist, US Card Management

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Principal Data Scientist, US Card Management

Location: McLean, Virginia, United States

Company: Capital One

About the Role

Join our team at Capital One and be at the forefront of the next wave of innovation in the credit card industry. Our Principal Data Scientist in US Card Management will leverage state-of-the-art technologies and machine learning to drive data-driven decision-making and improve our customers' financial lives.

Team Description

The US Card Management Data Science team is dedicated to developing industry-leading machine learning models to optimize credit card management decisions, including credit limit increases. We collaborate closely with data engineers, platform engineers, product managers, credit analysts, and business analysts to deliver comprehensive solutions from ideation to implementation. As creative problem solvers, we continuously innovate and enhance our models' dynamism, adaptability, robustness, and intelligence.

Role Responsibilities

  • Partner with a cross-functional team including data scientists, software engineers, and product managers to deliver customer-centric products.
  • Utilize a broad technology stack – Python, Conda, AWS, H2O, Spark, and more – to uncover insights within massive datasets.
  • Develop machine learning models through all phases of the lifecycle, from design and training to evaluation, validation, and implementation.
  • Translate complex data findings into actionable business objectives using strong interpersonal skills.

Ideal Candidate Profile

Customer-Centric

Passionate about making the right decisions for our customers, from analyzing data to creating solutions.

Innovative

Continuously researching and applying emerging technologies and methodologies to stay ahead in the field.

Creative

Adept at defining and solving large, complex problems with unique and effective solutions.

Technical Expertise

Proficient with open-source languages and cloud computing platforms, backed by hands-on experience in developing data science solutions.

Statistically Savvy

Experienced in building, validating, and backtesting models, and interpreting data analysis tools like confusion matrices or ROC curves. Skilled in clustering, classification, sentiment analysis, time series, and deep learning.

Data Enthusiast

Comfortable working with big data, with the ability to retrieve, combine, and analyze data from diverse sources and structures.

Basic Qualifications

  • Bachelor’s Degree plus 5 years of data analytics experience, or a Master’s Degree plus 3 years of data analytics experience, or a PhD with the required degree obtained before the start date.
  • At least 1 year of experience in open source programming languages for large-scale data analysis.
  • At least 1 year of machine learning experience.
  • At least 1 year of experience with relational databases.

Preferred Qualifications

  • Master’s Degree or PhD in a STEM field (Science, Technology, Engineering, Mathematics).
  • At least 1 year of experience with AWS.
  • At least 3 years of experience in Python, Scala, or R.
  • At least 3 years of machine learning experience.
  • At least 3 years of SQL experience.

Compensation

Salaries for full-time roles vary by location. For example, in New York City (Hybrid On-Site): $165,100 - $188,500 for Data Science PhDs. Other locations will have corresponding pay ranges, and actual salaries will be communicated in the offer letter. This role is also eligible for performance-based incentives, including cash bonuses and long-term incentives.

Benefits

Capital One offers a comprehensive, competitive, and inclusive benefits package supporting your overall well-being. Learn more at the Capital One Careers website. Eligibility for benefits varies based on full or part-time status, exempt or non-exempt status, and management level.

Diversity & Inclusion

Capital One is an equal opportunity employer committed to diversity and inclusion. All qualified applicants will receive consideration without regard to sex, race, color, age, national origin, religion, disability, genetic information, marital status, sexual orientation, gender identity