Data Science Director, Digital ML

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

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 Digital ML team improves customer experience and contributes to the growth of the organization through researching, developing and deploying complex, cutting edge and scalable machine learning solutions. Our team of high caliber data scientists partner with PMs and engineering to ensure that we are developing and deploying relevant and impactful ML based products. We leverage the wealth of Capital One’s data to build ML algorithms and rapidly experiment, learn and iterate. We work very closely with business stakeholders, collect requirements, and deliver high value ML solutions that drive customer and business value. Those of us who work with data see this as the pinnacle of opportunities that you cannot find anywhere in the industry. 

Role Description

We are looking for an outstanding ML leader who can partner with business stakeholders and identify/prioritize top ML opportunities, develop technical requirements, research and develop novel, scalable and production ready ML algorithms using creative / cutting edge methods. The ideal leader will combine expert ML and technology knowledge with hands-on experience developing and deploying/scaling models with outstanding business sense and communication skills.

The Ideal Candidate will:

• Interface with business stakeholders, engineers, and software developers to gather requirements and deliver complete ML solutions
• Own the design, development and deployment of ML algorithms
• Lead, mentor and manage scientists with diverse skills
• Closely follow publications, research and open source developments
• Understand, at a deep level, business, product, and platform strategies, goals, and objectives. Drive the ML road-map for product teams
• Have advanced knowledge in model evaluation, tuning and performance
• Work with projects involving large scale multidimensional databases, streaming data, complex business infrastructure, and cross-functional teams
• Work with cloud based data storage systems, model development and deployment environments, data transformation/feature creation, streaming data systems, partnering with technology/engineering to scale and deploy batch and real time models in the cloud (preferably AWS)

Basic Qualifications:

• Bachelor’s Degree plus 9 years of experience in data analytics, or Master’s Degree plus 7 years of experience in data analytics, or PhD plus 4 years of experience in data analytics
• At least 4 years of experience in open source programming languages for large scale data analysis
• At least 4 years of experience with machine learning
• At least 4 years of experience with relational databases

Preferred Qualifications:

• PhD in “STEM” field (Engineering, Computer Science, Mathematics, Computational Statistics, Operations Research, Machine Learning or related technical fields) plus 5 years of experience in data analytics
• At least 5 years of experience in Python, Spark, Scala, or R for large scale data analysis
• At least 3 years of experience with NLP, Reinforcement Learning, Deep Learning (CNNs, RNNs, embedding architectures)
• At least 3 successfully deployed and scaled ML use cases in big data and cloud compute/storage environment
• At least 1 deployed and scaled ML use cases in a streaming data environment
• At least 1 deployed and scaled ML use case in a real time inference and online learning environment

Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.