Principal Associate, Data Science, Model Risk Office

Capital One Financial

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Quick summary

Work type: On-site
Location: McLean, VARichmond, VA
Salary: $161,800–$184,600 / yr
Posted: 4 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $198k

This role $173k

$142k most similar roles pay here $246k

This role pays less than 62% of similar roles. Most pay $160,825–$235,700 — the shaded band above. At the midpoint, this role pays about $173k versus about $198k 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 1043 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 1037 roles with salary data.

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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 and improve decision-making through advanced analytics. 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 build machine learning models to challenge current production "champion" models, contribute to the governance framework for next-generation machine learning, and validate a wide variety of models across multiple business domains. To succeed, you must utilize a technical stack including Python, Conda, AWS, Spark, SQL, Scala, or R to analyze large volumes of numeric and textual data. You will apply expertise in clustering, classification, sentiment analysis, time series, and deep learning to solve complex risk management problems within the enterprise.

Skills

Python AWS Spark SQL Conda Machine Learning Deep Learning R Scala Big Data Time Series Sentiment Analysis Clustering Classification Statistics

What you'll do

Identify and quantify risks associated with models in collaboration with cross-functional teams.
Use Python, Conda, AWS, and Spark to extract insights from large volumes of numeric and textual data.
Build machine learning models to challenge "champion" models currently deployed in production.
Contribute to the model governance framework for next-generation machine learning models.
Present findings on how specific model risks impact business operations to executive leadership.
Validate a wide variety of models across multiple business domains within the Enterprise Services division.

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.
Experience building, validating, and backtesting models including clustering, classification, sentiment analysis, time series, and deep learning.
Proficiency in Python, Conda, AWS, Spark, and other open-source tools for data science solutions.
Ability to retrieve, combine, and analyze large volumes of numeric and textual data from various sources.
Master's degree in a STEM field plus 3 years of experience in data analytics, or a PhD in a STEM field (preferred).
At least 1 year of experience with AWS, 3 years with Python/Scala/R, 3 years with machine learning, and 3 years with SQL (preferred).

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