Manager, Data Science
About The Position
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. The Card DS 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.
Requirements
- Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date: A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 6 years of experience performing data analytics
- A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 4 years of experience performing data analytics
- A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 1 year of experience performing data analytics
- At least 1 year of experience leveraging open source programming languages for large scale data analysis
- At least 1 year of experience working with machine learning
- At least 1 year of experience utilizing relational databases
Nice To Haves
- PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics
- 3+ years of hands-on experience building, deploying, and maintaining high-scale, production-grade ML systems using MLOps practices, including AWS, Kubeflow, and CI/CD pipelines
- Deep expertise (4+ years) in developing and optimizing state-of-the-art Deep Learning models, specifically Transformer-based architectures, using PyTorch and distributed training with multi-GPU optimization
- Extensive experience (4+ years) with high-performance, distributed data processing for petabyte-scale feature engineering using frameworks like DASK and PySpark
Responsibilities
- 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
Benefits
- Comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being.
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What This Job Offers
Job Type
Full-time
Career Level
Manager
