Principal Associate, Data Scientist, SBB Fraud

AI summary of the role

This Principal Associate Data Scientist role on the Small Business Bank Fraud team builds machine learning models to protect customers and Capital One from fraud.

What you’ll do

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 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

What you’ll bring

Bachelor's degree in a quantitative field plus 5 years data analytics experience, or Master's plus 3 years, or PhD
At least 3 years experience with machine learning
At least 3 years experience in Python, Dask, Spark etc.
At least 3 years experience with SQL

Technologies

Python · Conda · AWS · H2O · Spark · Dask · SQL · deep learning · machine learning · statistical modeling

Source and classification

Internal deployment & tooling · Evidence for this classification:

account for 44% of the country's GDP. The Small Business Bank (SBB) Fraud Data Science team builds the machine learning models that help protect our customers and Capital One against fraudsters by catching millions of dollars in transactions, account opening, account takeover fraud. You will work with cross functional teams including analysts, software engineers and product managers to build, deploy and manage fraud models that make intelligent decisions. 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,

More from the job description

Principal Associate, Data Scientist, SBB Fraud 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 Small businesses make up 99% of businesses in the US and account for 44% of the country's GDP. The Small Business Bank (SBB) Fraud Data Science team builds the machine learning models that help protect our customers and Capital One against fraudsters by catching millions of dollars in transactions, account opening, account takeover fraud. You will work with cross functional teams including analysts, software engineers and product managers to build, deploy and manage fraud models that make intelligent decisions. Role Description In this role, you will: Pa [... source excerpt omitted ...] 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. 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. 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. Basic Qualifications: Currently has, or is in the process of obtaining one of the fo [... source excerpt omitted ...] ng data analytics A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) Preferred Qualifications: Master’s Degree in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics, or PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) At least 3 years’ experience with machine learning At least 1 year of experience with deep learning/emerging AI e.g. graphs, embeddings, sequence models, transformer models At least 3 years’ experience in Python, Dask, Spark etc. At least 3 years’ experience with SQL At least 1 year of ex

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Employer postings · Data from Sep 9 · Sources