Director, Data Science - Financial Services

Key Skills

Java Programming, Finance, Design, Python Programming, Database, SQL, AWS, Big Data Technology, Scala Programming, Machine learning techniques, Data science techniques, R Programming, Large scale data processing, Mathematics, Data Analysis

Job Description

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's Auto Finance business, you’ll be part of a high performing modeling and analytics team that’s leading the next wave of disruption at a whole new scale. Our team has a relentless focus on the craft of modeling and innovation, 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.

Capital One's Auto Finance business has been growing massively year over year on the heels of a robust strategic agenda advancing our technological capabilities to bring the best possible experience to our customers. We face the unique challenge of building products to directly serve dealerships and consumers, while also building robust modeling systems to manage our lending risks. The Data Science team is driving every stage of business strategy and product development, ranging from marketing and personalization, underwriting, pricing, valuation, loss forecasting, image recognition, text analytics.

In this role, you will:

• Build and deliver a comprehensive data science roadmap as it relates to marketing decisions ranging from our media budget to what ad to show to which person
• Partner with senior leaders and partner teams in problem framing and conception of ideas to develop consensus and execute on the machine learning agenda
• Lead a data science team and build machine learning models through all phases of development, from design, testing, data gathering, training, evaluation, validation, governance, and implementation
• Partner with a cross-functional team of product managers, software engineers, marketers, and business analysts to launch machine learning solutions into production
• Actively identify and manage model risks in line with model risk management policies through effective challenge of your team
• Identify, evaluate, and deploy new algorithms, data strategies, test plans, and implementation capabilities to drive continuous innovation
• Lead, recruit, and grow a team of data scientists to achieve their professional goals

The Ideal Candidate is:

• Customer first. You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it’s about making the right decision for our customers.
• 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.
• You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You’re not afraid to share a new idea.
• 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.
• 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.
• A data guru. “Big data” doesn’t faze you. You have the skills to retrieve, combine, and analyze data from a variety of sources and structures. You know understanding the data is often the key to great data science.

Basic Qualifications:

• Bachelor’s Degree plus 9 years of experience in data analytics, or Master’s Degree plus 7 years of experience in data analytics, or PhD plus 4 years of experience in data analytics
• At least 4 years of experience in open source programming languages for large scale data analysis
• At least 4 years of experience with machine learning
• At least 4 years of experience with relational databases

Preferred Qualifications:

• PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 5 years of experience in data analytics
• At least 1 year of experience working with AWS
• At least 3 year of experience managing people
• At least 5 years of experience in Python, Scala, or R for large scale data analysis
• At least 5 years of experience with machine learning