below marketmost similar roles pay hereabove market
This role pays less than
57%
of similar roles. Most pay
$160,400–$215,700
— the blue band above.
At the midpoint, this role pays about
$173k
versus about
$188k
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
2107 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 1107 roles with salary data.
JOB TITLE: Principal Associate, Data Scientist - Card Intelligence
The Principal Associate, Data Scientist - Card Intelligence joins the US Card Data Science organization to drive business strategy and enhance customer experiences. In this role, you will partner with cross-functional teams of data scientists, software engineers, and product managers to build and deploy sophisticated machine learning models across the credit card lifecycle, including marketing, acquisitions, underwriting, and fraud prevention. You will develop models through all phases of design, training, evaluation, and implementation while translating complex insights into tangible business goals. You will utilize a technical stack including Python, Conda, AWS, H2O, Spark, and SQL to analyze huge volumes of numeric and textual data. The work involves solving complex problems by leveraging big data to improve financial products and protect millions of cardholders daily.
What does a Data Scientist earn in Virginia?
Median $162000 from 89 postings across 9 companies.
Build machine learning models through all phases of development including design, training, evaluation, and implementation.
Leverage Python, AWS, Spark, and H2O to extract insights from large volumes of numeric and textual data.
Develop data science solutions to support marketing, acquisitions, underwriting, and fraud prevention.
Retrieve, combine, and analyze data from a variety of sources and structures to drive business strategy.
Translate complex technical work into tangible business goals for stakeholders and product managers.
Research and evaluate emerging technologies and state-of-the-art methods to improve existing data science applications.
Validate and backtest models using statistical methods such as clustering, classification, and time series analysis.
What we're looking for
Must have a Bachelor's degree in a quantitative field plus 5 years of data analytics experience.
Must have a Master's degree in a quantitative field or an MBA with a quantitative concentration plus 3 years of data analytics experience.
Must have a PhD in a quantitative field.
Must have hands-on experience developing data science solutions using open-source tools and cloud computing platforms.
Must have experience building, validating, and backtesting models using clustering, classification, sentiment analysis, time series, and deep learning.
Must have the skills to retrieve, combine, and analyze data from a variety of sources and structures.
Preferred: Master’s degree in a STEM field plus 3 years of experience in data analytics.
Preferred: PhD in a STEM field.
Preferred: At least 1 year of experience working with AWS.
Preferred: At least 3 years of experience in Python, Scala, or R.
Preferred: At least 3 years of experience with machine learning.
Preferred: At least 3 years of experience with SQL.
Python
AWS
Spark
H2O
SQL
Scala
R
Conda
Machine Learning
Deep Learning
Time Series
Sentiment Analysis
Clustering
Classification
Statistical Modeling
Big Data
Python
Snowflake
AWS
Spark
H2O
OneLake
SQL
Scala
R
Conda
Machine Learning
Deep Learning
Time Series
Sentiment Analysis
Clustering
Classification
Statistical Modeling
Process Automation
Python
Snowflake
AWS
Spark
H2O
OneLake
SQL
Scala
R
Conda
Machine Learning
Deep Learning
Time Series
Sentiment Analysis
Clustering
Classification
Statistical Modeling
Process Automation
Generative AI
Python
AWS
Spark
H2O
SQL
Scala
R
NLP
Deep Learning
Time Series
Machine Learning
Conda
Computer Vision
Speech Recognition
Statistical Modeling