Principal Associate, Data Science, Model Risk Office

Capital One Financial

Confirmed live yesterday Trusted

Quick summary

Work type: On-site
Location: McLean, VAChicago, ILNew York, NYPlano, TX
Salary: $147,100–$167,900 / yr
Posted: 96 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $202k

This role $158k

$136k most similar roles pay here $253k

This role pays less than 79% of similar roles. Most pay $162,025–$241,431 — the shaded band above. At the midpoint, this role pays about $158k versus about $202k for comparable roles.

Based on 239 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.

Most-posted roles

View all roles at Capital One Financial

At a glance

TL;DR · Principal Associate, Data Science, Model Risk Office

Role Posting Log in to save

Principal Associate, Data Science - Model Risk Office joins the Model Risk Office team to defend the company against model failures while identifying ways to improve decision-making through better models. In this role, you will partner with cross-functional teams of data scientists, software engineers, and product managers to identify and quantify risks associated with models. You will lead teams of data scientists building machine learning models to challenge current champion models in production and contribute to the model governance framework. You will also lead the validation of various models across multiple business domains. To succeed, you must utilize a technology stack including Python, Conda, AWS, H2O, Spark, and SQL. The role requires expertise in statistics, machine learning, clustering, classification, sentiment analysis, time series, and deep learning to manage risks within large volumes of numeric and textual data.

Skills

Python AWS Spark H2O SQL Conda Scala R Machine Learning Deep Learning Time Series Sentiment Analysis Clustering Classification Statistics Data Analytics Cloud Computing

What you'll do

Identify and quantify risks associated with models in collaboration with cross-functional teams.
Utilize Python, AWS, H2O, and Spark to extract insights from large volumes of numeric and textual data.
Lead teams of data scientists to build machine learning models that challenge current production models.
Contribute to the model governance framework for next-generation models.
Validate a wide variety of models across multiple business domains.
Present identified model risks and their potential impacts on the business to executive leadership.

What we're looking for

A Bachelor's degree in a quantitative field plus 5 years of experience performing data analytics.
A Master's degree in a quantitative field or an MBA with a quantitative concentration plus 3 years of experience performing data analytics.
A PhD in a quantitative field.
At least 1 year of experience working with AWS.
At least 3 years of experience with Python, Scala, or R.
At least 3 years of experience with machine learning.
At least 3 years of experience with SQL.
Experience building and validating models using techniques like clustering, classification, sentiment analysis, time series, and deep learning.

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