This role pays less than
70%
of similar roles. Most pay
$139,875–$211,200
— the shaded band above.
At the midpoint, this role pays about
$145k
versus about
$176k
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
1015 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 1008 roles with salary data.
As a Senior Associate, Data Scientist - Card Intelligence, you will join the US Card Data Science team to develop and deploy sophisticated machine learning models across the entire credit card lifecycle, including marketing, acquisitions, underwriting, and fraud prevention. You will collaborate with cross-functional teams of data scientists, software engineers, and product managers to translate complex insights into tangible business goals for millions of cardholders. Your daily work involves building models through all phases of development, from design and training to evaluation and implementation. To succeed, you must be proficient in Python, Conda, AWS, H2O, Spark, SQL, Scala, or R. You will analyze massive volumes of numeric and textual data using techniques such as clustering, classification, sentiment analysis, time series, and deep learning to solve complex problems within the credit card industry.
What does a Data Scientist earn in Virginia?
Median $145200 from 78 postings across 6 companies.
Build machine learning models through all phases of development including design, training, evaluation, and implementation.
Analyze large volumes of numeric and textual data using Python, Spark, H2O, and AWS.
Translate complex technical insights into tangible business goals for non-technical stakeholders.
Develop data science solutions to improve marketing, acquisitions, underwriting, and fraud prevention.
Retrieve and combine data from various sources and structures to uncover hidden insights.
Research and evaluate emerging technologies and state-of-the-art methods to apply to current projects.
Solve large, undefined problems by identifying key questions and developing innovative solutions.
What we're looking for
A Bachelor's degree in a quantitative field plus 2 years of experience performing data analytics.
A Master's degree in a quantitative field or an MBA with a quantitative concentration.
Experience building, validating, and backtesting machine learning models including clustering, classification, sentiment analysis, time series, and deep learning.
Proficiency in analyzing large volumes of numeric and textual data from various sources and structures.
Experience working with AWS (preferred).
At least 2 years of experience in Python, Scala, or R (preferred).
At least 2 years of experience with machine learning (preferred).
At least 2 years of experience with SQL (preferred).
Python
AWS
Machine Learning
Spark
H2O
SQL
Conda
Scala
R
Deep Learning
Sentiment Analysis
Time Series
Clustering
Classification
Big Data
Statistical Modeling
Python
AWS
Machine Learning
Spark
SQL
H2O
Conda
Scala
R
Deep Learning
Sentiment Analysis
Time Series
Clustering
Classification
Big Data
Statistical Modeling
Python
Snowflake
AWS
Spark
H2O
OneLake
SQL
Conda
Scala
R
Machine Learning
Deep Learning
Time Series
Sentiment Analysis
Clustering
Classification
Statistical Modeling
Data Analytics
Python
Snowflake
AWS
Spark
H2O
OneLake
SQL
Conda
Scala
R
Machine Learning
Deep Learning
Time Series
Sentiment Analysis
Clustering
Classification
Statistical Modeling
Data Analytics
Python
AWS
Spark
H2O
Conda
Machine Learning
Scala
R
Deep Learning
Time Series
Sentiment Analysis
Clustering
Classification
Relational Databases
Big Data