Senior Director, Data Science- Card

Key Skills

Java Programming, Design, Python Programming, Database, SQL, Scala Programming, Machine learning techniques, Data science techniques, Conda, R Programming, SPARK Programming, Statistical modeling, Large scale data processing, Mathematics, H2O, 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, 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:

The Card Intelligence team is working continuously to innovate the way we handle data to transform every corner of our business through analytics, infrastructure, valuations, and strategy. We build human-centric experiences for moments that matter throughout the customer journey, and we do this by harnessing data, technology, and talent to propel our business forward. To accomplish this goal, we design, build, and maintain the appropriate solutions we use across Card to make smart, informed decisions. We use the latest techniques in machine learning to build predictive models and utilize the advantages of large-scale data and cloud-based processing. Through these techniques, we extract relevant insights from data to tackle a huge variety of business problems.

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:

• 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.
• 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:

• 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