below marketmost similar roles pay hereabove market
This role pays more than
86%
of similar roles. Most pay
$162,000–$223,750
— the blue band above.
At the midpoint, this role pays about
$246k
versus about
$193k
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
2276 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 1147 roles with salary data.
The Senior Manager, Data Science - Model Risk joins the Model Risk Office to defend the company against model failures and improve decision-making. Working within a cross-functional team of data scientists, software engineers, and product managers, the role involves building machine learning models through all phases of development, including design, training, evaluation, validation, and implementation. The position requires leveraging a broad technology stack including Python, Conda, AWS, H2O, Spark, and relational databases to reveal insights within large volumes of numeric and textual data. Key technical competencies include clustering, classification, sentiment analysis, time series, and deep learning. The role addresses the business problem of identifying and improving the status quo to ensure reliable outcomes in Risk Management and the broader enterprise.
What does a Data Science Manager earn?
Median $211200 from 46 postings across 11 companies.
Build machine learning models through all phases of development including design, training, evaluation, validation, and implementation.
Leverage a broad technology stack including Python, AWS, H2O, and Spark to extract insights from large volumes of data.
Analyze and interpret complex numeric and textual data to reveal hidden business insights.
Translate complex technical work into tangible business goals for non-technical stakeholders.
Research and evaluate emerging technologies and state-of-the-art methods to improve existing applications.
Defend the company against model failures by identifying and improving the status quo of risk management.
Manage and develop the talents of team members and broader organizational partners.
What we're looking for
Must have a Bachelor's in a quantitative field plus 7 years of data analytics experience, a Master's plus 5 years, or a PhD plus 2 years.
Must have at least 2 years of experience leveraging open source programming languages for large scale data analysis.
Must have at least 2 years of experience working with machine learning.
Must have at least 2 years of experience utilizing relational databases.
Must have experience with clustering, classification, sentiment analysis, time series, and deep learning.
Must be able to build machine learning models through all phases of development, including design, training, evaluation, validation, and implementation.
PhD in a STEM field plus 4 years of experience in data analytics (preferred).
At least 1 year of experience working with AWS (preferred).
At least 1 year of experience managing people (preferred).
At least 5 years of experience in Python, Scala, or R for large scale data analysis (preferred).
At least 5 years of experience with machine learning (preferred).
Python
AWS
Spark
H2O
Conda
Scala
R
Machine Learning
Relational Databases
Deep Learning
Time Series
Sentiment Analysis
Clustering
Classification
Statistics
Data Analytics
Python
AWS
Spark
H2O
Conda
Scala
R
Machine Learning
Relational Databases
Deep Learning
Time Series
Sentiment Analysis
Clustering
Classification
Statistics
Data Analytics
Python
AWS
Spark
H2O
Conda
Scala
R
Machine Learning
Deep Learning
Relational Databases
Statistical Modeling
Time Series
Sentiment Analysis
Clustering
Classification
Data Analytics
Big Data
Python
AWS
Spark
H2O
SQL
Scala
R
Conda
Relational Databases
Machine Learning
Deep Learning
Time Series
Sentiment Analysis
Clustering
Classification
Statistical Modeling
Big Data
Data Analytics
Python
Scala
R
PyTorch
Hugging Face
LangChain
Vector Databases
LLMOps
AWS
Machine Learning
Deep Learning
Statistics
Time Series
Sentiment Analysis
Clustering
Classification
Python
AWS
Spark
H2O
SQL
Scala
R
NLP
Generative AI
Machine Learning
Conda
Deep Learning
Time Series
Sentiment Analysis
Clustering
Classification
ROC Curve
Confusion Matrix