Manager, Data Science - AI Foundations
About The Position
Capital One’s AI Foundations Specialist Models Data Science team builds and ships scalable architecture and AI/ML solutions for the company’s mobile app. The team partners with product, technology, and design teams to deliver dynamic, personalized customer experiences, including features for the digital assistant Eno and content search. The role will lead experimentation and innovation in next-generation experiences powered by generative AI. The manager partners with data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products. The position uses technologies such as PyTorch, AWS Ultraclusters, Hugging Face, LangChain, Lightning, and VectorDBs to work with large volumes of numeric and textual data. It applies NLP and LLMs to customer-facing applications, and builds models through design, training, evaluation, and validation, working with engineering teams to deploy them in scalable, resilient systems serving more than 80 million customers. The role calls for an experienced technical leader who can communicate complex work in terms of business goals, develop talent, influence cross-functional teams, and deliver models and related code, libraries, or platforms at scale. The successful candidate has experience training language models or large computer vision models and expertise in areas such as training optimization, self-supervised learning, explainability, or RLHF. Applicants must have a quantitative degree or be obtaining one by the scheduled start date, along with the specified data analytics experience: a bachelor’s degree plus 6 years, a master’s degree or quantitatively concentrated MBA plus 4 years, or a PhD plus 1 year. Applicants also need at least 1 year of experience with open-source programming languages for large-scale data analysis, machine learning, and relational databases. The position may be eligible for employment authorization sponsorship. The role is eligible for performance-based incentive compensation. Salary ranges vary by listed location.
Requirements
- Currently hold or be obtaining by the scheduled start date a bachelor’s degree in a quantitative field plus 6 years of data analytics experience; 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.
- Experience training language models or large computer vision models.
- Expertise in at least one key subdomain such as training optimization, self-supervised learning, explainability, or RLHF.
- Experience delivering models at scale in training data and inference volumes.
- Experience delivering libraries, platforms, or solution-level code to existing products.
- Hands-on experience with LLMs, open-source tools, and cloud computing platforms.
- Strong communication and interpersonal skills to explain findings to non-technical audiences and influence cross-functional teams.
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
- Partner with data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products.
- Use technologies including PyTorch, AWS Ultraclusters, Hugging Face, LangChain, Lightning, and VectorDBs to analyze large volumes of numeric and textual data.
- Apply NLP and LLMs to customer-facing applications and features, including adapting and fine-tuning models.
- Build machine learning and NLP models from design through training, evaluation, and validation.
- Partner with engineering teams to operationalize models in scalable, resilient production systems.
- Translate technical complexity into tangible business goals.
- Research and evaluate emerging technologies and identify opportunities to apply them.
- Lead and influence cross-functional teams in AI/ML innovations, challenge conventional thinking, and contribute to talent development.
- Deliver models at scale in training data and inference volumes, as well as libraries, platforms, or solution-level code for existing products.
Benefits
- Comprehensive health, financial, and other benefits supporting total well-being; eligibility varies by employment status and management level.
- Performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.
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What This Job Offers
Job Type
Full-time
Career Level
Manager
