Capital One is looking to leverage technology to improve operational efficacy and provide an extraordinary member experience for its Velocity Black concierge product by building personalization and recommendation models
Requirements
- 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
- Strong proficiency in Python and SQL for large scale data analysis
- At least 1 year of experience preparing and transforming large, messy, or sparse datasets for modeling
- At least 1 year of experience designing, training, and deploying large-scale models in production
- Familiarity with sophisticated ML approaches to personalization models or recommendation systems
Responsibilities
- Lead the design, development, and maintenance of personalization and recommendation models
- Apply modern AI/ML methods (e.g. collaborative filtering, ensemble retrievers, embeddings, neural networks, LLM as a judge, etc.) to optimize and elevate member experiences
- Collaborate with product/business and engineering to integrate models into our platform, including model registration and ongoing measurement
- Develop evaluation frameworks to monitor performance, bias, and accuracy of recommendations
- Champion data-driven decision making across the team
Other
- 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, A Master's Degree in a quantitative field, or a PhD in a quantitative field
- At least 6 years of experience performing data analytics for Bachelor's degree, 4 years for Master's degree, or 1 year for PhD
- Customer first mindset, creative, leader, and passionate about talent development
- Ability to influence stakeholders and lead technical strategy
- Travel requirements not specified, but remote work is eligible
