Manager, Data Science

Manager, Data Science

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.

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 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 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

Preferred Qualifications:

• 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