Senior Manager, Data Science - Model Risk Office

Capital One; New York (NY); On-site; USD 250,800 - 286,200; Full time

Benefits offered by this job

Comprehensive health benefits

Competitive financial benefits

Opportunities for professional development

Job summary

Capital One is seeking a Senior Manager, Data Science to join their Model Risk Office in New York. This role emphasizes evaluating Card Fraud models, utilizing innovative machine learning techniques, and mentoring junior data scientists.

The ideal candidate will possess a quantitative degree and significant experience in data analytics and model development. Capital One offers competitive compensation and a comprehensive benefits package designed to support your well-being.

Qualifications

• 7 years of experience performing data analytics.
• At least 2 years of experience working with machine learning.
• Hands-on experience developing data solutions using open-source tools.

Responsibilities

• Partner with a cross-functional team to deliver a product customers love.
• Build machine learning models through all phases of development.
• Translate complex work into tangible business goals.

Skills

Machine Learning

Data Analysis

Python

Statistics

Communication

Education

Bachelor's degree in a quantitative field

Master's degree in a quantitative field

PhD in a quantitative field

Tools

AWS

H2O

Spark

Conda

Job description

Senior Manager, Data Science - Model Risk Office

Data is at the center of everything we do. As a startup, we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and the relational database, cutting edge technology in 1988! Fast-forward a few years, and this little innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision‑making.

As a Data Scientist at Capital One, you’ll be part of a team that’s leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives.

Team Description

In Capital One’s Model Risk Office, we defend the company against model failures and find new ways of making better decisions with models. We use our statistics, software engineering, and business expertise to drive the best outcomes in both Risk Management and the Enterprise.

The successful candidate will join the Card Fraud Model Risk team, which is tasked with evaluating risk for Card Fraud models used throughout the customer lifecycle. This role covers fraud detection at various stages, including application, transaction, and payment, for both first‑party and third‑party use cases. The team conducts thorough assessments of existing fraud models and builds independent challenger models to ensure effective oversight.

Working within a small, innovative group, you will act as a key individual contributor investigating advanced algorithms such as graph, neural, tree‑based, and sequence methods to create robust challenger models. Additionally, you will provide mentorship to junior data scientists as you explore cutting‑edge solutions and oversee card fraud model risk.

Role Description

In this role, you will:

• 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 the insights hidden within huge volumes of numeric and textual data
• Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
• Flex your interpersonal skills to translate the complexity of your work into tangible business goals

The Ideal Candidate is:

• Innovative. You continually research and evaluate emerging technologies. You stay current on published state‑of‑the‑art methods, technologies, and applications and seek out opportunities to apply them.
• A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You’re passionate about talent development for your own team and beyond.
• Technical. You’re comfortable with open‑source languages and are passionate about developing further. You have hands‑on experience developing data science solutions using open‑source tools and cloud computing platforms.
• Statistically‑minded. You’ve built models, validated them, and backtested them. You know how to interpret a confusion matrix or a ROC curve. You have experience with clustering, classification, sentiment analysis, time series, and deep learning.

Basic Qualifications:

• Currently has, or is in the process of obtaining one of the following with an expectation that 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 7 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 5 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) plus 2 years of experience performing data analytics
• At least 2 years of experience leveraging open source programming languages for large scale data analysis
• At least 2 years of experience working with machine learning
• At least 2 years of experience utilizing relational databases

Preferred Qualifications:

• PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 4 years of experience in data analytics
• At least 1 year of experience working with AWS
• At least 1 year of experience managing people
• At least 5 years’ experience in Python, Scala, or R for large scale data analysis
• At least 5 years’ experience with hands‑on Machine Learning model development and deployment
• Experience with model development/deployment pipeline (e.g. Kubeflow, Kubernetes) is preferred.

Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.

Compensation and Benefits

McLean, VA: $229,900 - $262,400 for Sr Mgr, Data Science

New York, NY: $250,800 - $286,200 for Sr Mgr, Data Science

Richmond, VA: $209,000 - $238,500 for Sr Mgr, Data Science

Riverwoods, IL: $209,000 - $238,500 for Sr Mgr, Data Science

Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well‑being. Learn more at the Capital One Careers website. Eligibility varies based on full or part‑time status, exempt or non‑exempt status, and management level.

Equal Employment Opportunity Statement

Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non‑discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug‑free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23‑A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901‑4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.

If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at [e-mail] . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.