Sr. Director, Data Science - Apollo Team (Remote-Eligible)

Key Skills

Algorithms, AWS, Data Analysis, Database, Deep Learning, Java Programming, Large scale data processing, Machine learning techniques, Python Programming, PyTorch, R Programming, Scikit-learn, SQL

Job Description

Emerging Merchant Businesses is a new team dedicated to discovering and accelerating high-growth opportunities at Capital One. Our newly formed organization is composed of incredibly exciting Capital One businesses, including Capital One Shopping, Flex Pay, and Apollo. As part of the EMB team, you will work alongside experts across customer experience, data, technology (and more!) who are moving quickly to find the next big business at the company. We are looking for talented associates who are motivated to be part of this journey and help us shape the future of Capital One, growing and learning with a team who will invest in your development, foster an inclusive environment, and lead with heart to change banking for good.

As a Sr. Director, Data Scientist on the Apollo team in Emerging Merchant Businesses, you are a part of a team that develops the core technology which drives our industry-leading understanding of the U.S. business landscape, generating valuable business insight from many disparate sources. The Apollo team is building technology that allows us to acquire, understand, and make inferences on large, disparate business data sets. This requires ideas from across the machine learning skillset, applied to problems and data that are unique to our business. You will do it all in a collaborative environment that values your insight, encourages you to take on new responsibilities, promotes continuous learning, and rewards innovation.

In this role, you will:

• Lead a cross-functional team of data scientists, machine learning engineers, data engineers, business analysts and product managers to deliver product(s) customers love.
• Lead machine learning and data science technical direction and execution (operations, governance, processes and practice) working closely with product management to craft a roadmap and success criterion.
• Lean on your strong background in graph-based machine learning, deep learning, software engineering and algorithm development to organize, grow and manage the ML/AI capabilities for the product.
• Flex your interpersonal skills to translate the complexity of your work into tangible business goals

The ideal Candidate is:

Technical:

• Strong background in algorithmic and engineering practices in fields of machine learning including entity resolution, graph-based ML or deep learning. You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms.

Innovative:

• Learns constantly, including developments in algorithms, libraries, frameworks and ML technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them.

Customer first:

• You bridge the gap between business/product needs with ML design, engineering and execution. You identify the KPIs, resource needs, trade-offs and translate that into a ML roadmap.

Creative:

• You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You’re not afraid to share a new idea.

A Strategic Thinker:

• You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You can set the team vision and make strong day-to-day decisions backed by that vision. 

Inspiring Leadership:

• You establish a culture of inclusiveness, cooperation and candor. You’re passionate about talent development, provide frequent actionable feedback to team members, and promote innovation.

A Data Guru:

• “Big data doesn’t faze 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.

Statistically-Minded:

• You’ve 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, and deep learning.

A successful Candidate will have: 

• Experience setting and implementing broad strategic vision for ML applications, underlying platforms, and software 
• Demonstrated technical expertise in building, testing, deploying and scaling of machine learning software. Experience with cloud, open source software packages, environment management, reproducibility, and governance.
• High motivation, adaptability and being collaborative. An individual who can handle ambiguity and enjoys building. Moving in a fast-paced environment excites you and are looking for an opportunity to be a member of an innovative team that will be building and implementing innovative products in banking.
• Excellent communication skills. You can present technical context in intuitive ways. You can read the audience and adapt your communication style to various stakeholders both internal and external to the Apollo organization.

Capital One is open to hiring a Remote Employee for this opportunity. ?

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:

• At least 1 year of experience creating and executing multi-year technical initiatives.
• At least 1 year of experience with containerization technologies and understanding on how to build for cloud scale delivery is a plus
• PhD in “STEM field (Science, Technology, Engineering, or Mathematics) plus 5 years of experience in data analysis
• At least 1 year of experience working with AWS.
• At least 3 year of experience managing people and leading cross-functional teams
• At least 4 years of experience with relational databases with strong SQL knowledge
• At least 5 years of experience with technologies such as python, sci-kit learn, PyTorch, Scala, or R for large scale data analysis
• At least 5 years of experience with machine learning
• At least 5 years experience in algorithmic and engineering practices in fields of machine learning including entity resolution, graph-based ML or deep learning