This role pays less than
79%
of similar roles. Most pay
$202,612–$259,111
— the shaded band above.
At the midpoint, this role pays about
$192k
versus about
$231k
for comparable roles.
Based on 240 similar postings.
Employer
About Capital One Financial
Capital One Financial is a bank holding company specializing in credit cards, auto loans, banking, and savings products, known for its data-driven approach to consumer and commercial finance. Industry: Financial Services & Banking
Capital One Financial currently has
936 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 933 roles with salary data.
Manager, Data Scientist -Advanced Recommenders and Personalization Systems (Transformers, LLMs & Reinforcement Learning) joins a team building next-generation large-scale reinforcement learning based recommender systems and arbitration engines to power personalized experiences across marketing, customer servicing, and digital products. The role involves designing and developing state-of-the-art recommender systems using transformers, large language models (LLMs), and reinforcement learning. Key responsibilities include advancing personalization through sequence modeling, multi-modal learning, and contextual decision-making like Multi-Armed Bandits; building and fine-tuning foundation models on customer interaction data; and developing evaluation frameworks for campaign efficacy. The position requires proficiency in Python, SQL, Scala, or R, along with experience in PyTorch, TensorFlow, and cloud computing platforms. This role addresses the technical challenge of delivering real-time, intelligent experiences by leveraging rich credit and behavioral data to optimize long-term user engagement across mobile, web, and email channels.
Design and develop state-of-the-art recommender systems using transformers, large language models, and reinforcement learning.
Advance personalization through sequence modeling, multi-modal learning, and contextual decision-making like Multi-Armed Bandits.
Build and fine-tune foundation models trained on large-scale customer interaction data.
Apply reinforcement learning to optimize long-term user engagement and business outcomes.
Develop state-of-the-art evaluation frameworks to measure the efficacy of campaigns and recommendation systems.
Collaborate with engineering and product teams to deploy scalable, production-grade, low-latency systems.
Drive innovation in generative recommendations, conversational systems, and cross-domain personalization.
What we're looking for
A Bachelor's degree in a quantitative field plus 6 years of experience, a Master's in a quantitative field or MBA plus 4 years, or a PhD plus 1 year.
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 background in machine learning, deep learning, and recommender systems.
Hands-on experience with transformers, LLMs, or reinforcement learning.
Solid programming skills in Python and modern ML frameworks like PyTorch or TensorFlow.
Experience working with large-scale datasets and distributed training; AWS experience (preferred); 4+ years of experience in Python, Scala, R, machine learning, or SQL (preferred).