Manager, Data Science - Emerging ML

Capital One • McLean, VA

2d

About The Position

Emerging ML is the data science and machine learning team within Capital One’s Applied Research organization. The team researches and develops artificial intelligence technologies, with a focus on embeddings and foundation models, and partners with product and engineering teams to connect emerging technologies to business-critical use cases across Capital One.

This individual contributor role works across the data science lifecycle: designing, training, evaluating, and validating machine learning models, then partnering with engineering teams to operationalize them in scalable, resilient production systems serving more than 50 million customers. The role also partners with business and product teams on experiments to improve customer experiences and business outcomes in areas such as marketing, servicing, and fraud prevention.

The position involves writing software, such as Python or Scala, to collect, explore, visualize, and analyze numerical and textual data at large scale using tools such as Spark and AWS. Relevant team work includes researching self-supervised learning, transformer models, and representation learning; building customer behavioral models from transaction, clickstream, and other data; and refining integration patterns for encoder and decoder models for downstream use cases.

The ideal candidate is curious, creative, comfortable defining ambiguous problems, and able to communicate findings clearly to non-technical audiences. They have hands-on experience developing data science solutions from concept to production using open-source tools and modern cloud platforms, strong statistical modeling and validation skills, and a customer- and product-oriented perspective.

Requirements

• Currently has, or is in the process of obtaining by the scheduled start date, a bachelor’s degree in a quantitative field plus 6 years of experience performing data analytics; or a master’s degree in a quantitative field or an MBA with a quantitative concentration plus 4 years of data analytics experience; or a PhD in a quantitative field plus 1 year of data analytics experience.
• 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.
• Hands-on experience developing data science solutions from concept to production using open-source tools and modern cloud computing platforms.
• Experience building, validating, and backtesting statistical models, and interpreting measures such as confusion matrices and ROC curves.
• Experience with clustering, classification, sentiment analysis, time series analysis, and deep learning.
• Strong communication skills, including the ability to share findings with non-technical audiences.

Nice To Haves

• PhD in a STEM field (Science, Technology, Engineering, or Mathematics).
• Experience working with AWS.
• At least 4 years of experience in Python, Scala, or R.
• At least 4 years of experience with machine learning.
• At least 4 years of experience with SQL.

Responsibilities

• Research self-supervised learning, transformer models, representation learning, and other emerging AI technologies.
• Build customer behavioral models using transaction, clickstream, and other data to identify trends, patterns, and relationships related to product usage.
• Refine integration patterns for encoder and decoder models to connect Applied Research products with downstream business use cases.
• Build machine learning models through design, training, evaluation, and validation.
• Partner with engineering teams to operationalize models in scalable, resilient production systems serving more than 50 million customers.
• Partner with business and product teams to conduct experiments that improve customer experiences and business outcomes in marketing, servicing, and fraud prevention.
• Write software to collect, explore, visualize, and analyze large-scale numerical and textual data using tools such as Spark and AWS.
• Communicate findings clearly to non-technical audiences.

Benefits

• Comprehensive health, financial, and other benefits supporting total well-being.
• Eligible for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.

What This Job Offers

• Job Type: Full-time
• Career Level: Manager