Principal Associate, Data Scientist - Mainstreet Acquisitions
AI summary of the role
Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love
What you’ll do
- Leverage a broad stack of technologies - Python, Conda, AWS, H2O, Spark, and more - to reveal 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 interpersonal skills to translate the complexity of your work into tangible business goals
What you’ll bring
- Bachelor's Degree in a quantitative field plus 5 years of data analytics experience, or Master's plus 3 years, or PhD in a quantitative field
- At least 3 years of experience with Python
- At least 3 years of experience with SQL
- At least 3 years of experience with machine learning
Technologies
Python · Conda · AWS · H2O · Spark · SQL · machine learning · clustering · classification · deep learning
Source and classification
Internal deployment & tooling · Evidence for this classification:
builds industry-leading machine learning models that empower core underwriting decisions. We collaborate closely with a wide range of cross-functional partner teams - data engineers, platform engineers, product managers, credit, and business analysts - to deliver solutions from ideation to implementation. The Mainstreet Acquisitions team focuses on acquisitions and retention growth through next level personalization and enhanced decisioning. The associate is responsible for leading a workstream building the next generation of machine learning models used for credit decisioning. These models will be used for critical business decisions, such as card application approve/decline, product optimization, customer valuation, and more. 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
More from the job description
Principal Associate, Data Scientist - Mainstreet Acquisitions 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 US Card Intelligence Segments organization builds industry-leading machine learning models that empower core underwriting decisions. We collaborate closely with a wide range of cross-functional partner teams - data engineers, platform engineers, product managers, credit, and business analysts - to deliver solutions from ideation to implementation. The Mainstreet Acquisitions team focuses on acquisitions and retention growth through next level personalization and enhanced decisioning. The associate is responsible for leading a workstream b [... source excerpt omitted ...] of machine learning models used for credit decisioning. These models will be used for critical business decisions, such as card application approve/decline, product optimization, customer valuation, and more. 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 inter [... source excerpt omitted ...] irst. 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. 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. A data guru. “Big data” does
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