This role pays more than
79%
of similar roles. Most pay
$134,437–$211,200
— the shaded band above.
At the midpoint, this role pays about
$211k
versus about
$173k
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
994 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 987 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. You will partner with a cross-functional team of data scientists, software engineers, and product managers to deliver products by building machine learning models through all phases of development, including design, training, evaluation, validation, and implementation. The role involves extracting insights from large volumes of numeric and textual data to solve complex business problems within the credit card industry. You will utilize a technical stack including Python, Conda, AWS, H2O, Spark, PyTorch, DASK, and PySpark. Key skills include experience with clustering, classification, sentiment analysis, time series analysis, and deep learning. You must be able to translate complex technical findings into tangible business goals for non-technical audiences while managing petabyte-scale data.
Build machine learning models through all phases of development including design, training, evaluation, validation, and implementation.
Utilize a broad stack of technologies like Python, AWS, H2O, and Spark to uncover insights in large volumes of data.
Develop and maintain production-grade ML systems using MLOps practices, including Kubeflow and CI/CD pipelines.
Perform feature engineering on petabyte-scale datasets using distributed processing frameworks like DASK and PySpark.
Design and optimize state-of-the-art deep learning models, specifically Transformer-based architectures, using PyTorch.
Translate complex technical findings into tangible business goals for non-technical audiences.
Solve large, undefined problems by extracting relevant insights from numeric and textual data to address business needs.
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-grade 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
H2O
Spark
Conda
SQL
Scala
R
Machine Learning
Deep Learning
Relational Databases
Big Data
Sentiment Analysis
Time Series
Clustering
Classification
Statistical Modeling