This role pays more than
76%
of similar roles. Most pay
$159,000–$211,200
— the shaded band above.
At the midpoint, this role pays about
$211k
versus about
$185k
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
1756 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 961 roles with salary data.
Manager, Data Science joins the Applied AI team within AI Foundations to develop personalized customer experiences for web and mobile applications. This role involves architecting and deploying high-scale machine learning models, including recommendation engines powered by research into Foundation Models, Reinforcement Learning, Causal Inference, and Transformer-based architectures. You will collaborate with cross-functional teams of data scientists, software engineers, and product managers to build models through all phases of development, from design and training to evaluation and implementation. The work involves processing massive volumes of numeric and textual data to solve complex problems in the financial sector. Required technical skills include Python, Conda, AWS, H2O, Spark, and SQL. Candidates must be proficient in open-source languages for large-scale analysis, utilizing techniques such as clustering, classification, sentiment analysis, time series, and deep learning to drive meaningful business outcomes.
Architect and deploy high-scale machine learning models for personalized customer recommendation engines.
Develop machine learning models through all phases including design, training, evaluation, validation, and implementation.
Analyze large volumes of numeric and textual data using Python, Spark, and AWS to uncover hidden insights.
Conduct original research into Foundation Models, Reinforcement Learning, Causal Inference, and Transformer-based architectures.
Translate complex technical findings into tangible business goals for stakeholders and product managers.
Manage the end-to-end lifecycle of data science solutions using open-source tools and cloud platforms.
Identify and solve large, undefined problems by applying state-of-the-art methods to real-world challenges.
What we're looking for
A Bachelor's degree in a quantitative field plus 6 years of experience performing data analytics.
A Master's degree in a quantitative field or an MBA with a quantitative concentration plus 4 years of experience performing data analytics.
A PhD in a quantitative field plus 1 year of experience performing data analytics.
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.
A PhD in a STEM field plus 3 years of experience in data analytics (preferred).
At least 4 years of experience in Python, Scala, or R for large scale data analysis; at least 4 years of experience with machine learning; and at least 4 years of experience with SQL (preferred).
Python
SQL
AWS
Spark
H2O
Conda
Scala
R
Machine Learning
Deep Learning
Reinforcement Learning
Causal Inference
Relational Databases
Big Data
Sentiment Analysis
Time Series
Clustering
Classification
Python
AWS
Spark
SQL
H2O
Conda
Scala
R
Machine Learning
Deep Learning
Reinforcement Learning
Causal Inference
Relational Databases
Big Data
Sentiment Analysis
Time Series
Clustering
Classification