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Team Description
As part of Emerging ML, you will work on things like:
Conducting research into self supervised learning, transformer models, and representation learning
Building customer behavioral models (using transaction, clickstream, and other data) that identify trends, patterns, and relationships related to product usage
Refining integration patterns for encoder and decoder models for downstream use cases to connect Applied Research products and business use cases
Role Description
This is an individual contributor position. In Emerging ML, you will work at all phases of the data science lifecycle, including:
Build machine learning models through all phases of development, from design through training, evaluation and validation, and partner with engineering teams to operationalize them in scalable and resilient production systems that serve 50+ million customers.
Partner closely with a variety of business and product teams across Capital One to conduct the experiments that guide improvements to customer experiences and business outcomes in domains like marketing, servicing and fraud prevention.
Write software (Python, Scala, e.g.) to collect, explore, visualize and analyze numerical and textual data (billions of customer transactions, clicks, payments, etc.) using tools like Spark and AWS.
The Ideal candidate will be:
Curious and creative. You thrive on bringing definition to big, undefined problems. You love asking questions, and you love pushing hard to find the answers. You’re not afraid to share a new idea. You communicate clearly and effectively to share your findings with non-technical audiences.
Technical: You have hands-on experience developing data science solutions from concept to production using open source tools and modern cloud computing platforms. You are not afraid of petabytes of data.
Statistically-minded. You have 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 analysis and deep learning.
Customer and product oriented. You share our passion for changing banking for good.
Basic Qualifications:
Currently has, or is in the process of obtaining a Bachelor’s Degree plus 6 years of experience in data analytics, or currently has, or is in the process of obtaining a Master’s Degree plus 4 years of experience in data analytics, or currently has, or is in the process of obtaining PhD plus 1 year of experience in data analytics, with an expectation that required degree will be obtained on or before the scheduled start date
At least 2 year of experience in open source programming languages for large scale data analysis
At least 2 year of experience with machine learning
At least 2 year of experience with relational databases
Preferred Qualifications:
PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 3 years
of experience in data analytics
Experience building transformer models at scale (>100M parameters)
Understanding of self-supervised learning methods
Strong foundation in software engineering
At least 1 year of experience working with AWS
At least 4 years’ experience in Python, Scala, or R for large scale data analysis
At least 4 years’ experience with machine learning
At least 4 years’ experience with SQL
New York City, New York (Hybrid On-Site): $201,400 - $229,900 for Manager, Data Science
San Francisco, California (Hybrid On-site): $213,400 - $243,500 for Manager, Data Science
New York City (Hybrid On-Site): $201,400 - $229,900 for Mgr, Data Science San Francisco, California (Hybrid On-Site): $213,400 - $243,500 for Mgr, Data Science. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.
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 . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.
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