This role pays more than
77%
of similar roles. Most pay
$137,912–$211,200
— the shaded band above.
At the midpoint, this role pays about
$211k
versus about
$175k
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
998 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 992 roles with salary data.
Manager, Data Science joins the Card DS team to innovate how the company handles data through analytics, infrastructure, valuations, and strategy. Working within a cross-functional team of data scientists, software engineers, and product managers, you will build machine learning models through all phases of development, including design, training, evaluation, validation, and implementation. You will process massive volumes of numeric and textual data to solve complex business problems and provide actionable insights for the customer journey. The role requires proficiency in Python, Conda, AWS, H2O, Spark, PyTorch, DASK, and PySpark. Candidates must possess experience with relational databases, deep learning, sentiment analysis, time series analysis, and clustering. You will utilize these tools to develop production-grade systems while translating complex technical findings into tangible goals for non-technical audiences across the credit card industry.
Build machine learning models through all phases of development including design, training, evaluation, validation, and implementation.
Use Python, Spark, and AWS to extract insights from large volumes of numeric and textual data.
Develop data science solutions from initial concept to production using open source tools and cloud platforms.
Perform feature engineering on petabyte-scale datasets using distributed processing frameworks like PySpark or Dask.
Translate complex technical findings into tangible business goals for non-technical audiences.
Design and maintain scalable machine learning systems using MLOps practices, including CI/CD pipelines.
Develop and optimize state-of-the-art deep learning models, specifically Transformer-based architectures.
Solve large, undefined business problems by identifying and analyzing data to drive informed decision-making.
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.
PhD in a STEM field plus 3 years of experience in data analytics (preferred).
3+ years of experience building, deploying, and maintaining high-scale production ML systems using MLOps practices including AWS, Kubeflow, and CI/CD pipelines (preferred).
4+ years of experience developing and optimizing state-of-the-art Deep Learning models like Transformer-based architectures using PyTorch (preferred).
4+ years of experience with high-performance, distributed data processing for petabyte-scale feature engineering using DASK and PySpark (preferred).
Python
AWS
Spark
H2O
Conda
Machine Learning
Scala
R
Deep Learning
Time Series
Sentiment Analysis
Clustering
Classification
Relational Databases
Big Data