Manager, Data Scientist, Alternative Data Strategy

Capital One Financial

Confirmed live today High trust

Hybrid

Quick summary

Work type: Hybrid
Location: New York, NYMcLean, VARichmond, VAChicago, IL
Salary: $179,400–$204,700 / yr
Employment: Full-time
Posted: 6 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $176k

This role $192k

$119k $233k

below market most similar roles pay here above market

This role pays more than 64% of similar roles. Most pay $140,750–$211,200 — the blue band above. At the midpoint, this role pays about $192k 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 2107 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 1107 roles with salary data.

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View all roles at Capital One Financial

At a glance

TL;DR · Manager, Data Scientist, Alternative Data Strategy

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Manager, Data Scientist - Alternative Data Strategy joins the Alternative Data Strategy team to drive credit decisioning forward by integrating non-traditional datasets into US Card strategies. The role involves evaluating emerging vendors and data products, engineering high-impact features, and building scalable processing and monitoring systems. You will serve as a data domain expert, navigating risk governance for vendor models and building challenger models to enhance existing ones. Day-to-day tasks include building machine learning models through all phases of development, from design to implementation. You will utilize a broad technology stack including Python, Conda, AWS, H2O, Spark, and SQL to analyze huge volumes of numeric and textual data. Key skills include experience with clustering, classification, sentiment analysis, time series, and deep learning to solve complex credit decisioning problems.

Skills

Python AWS Spark H2O SQL Scala R Conda Relational Databases Machine Learning Deep Learning Time Series Sentiment Analysis Clustering Classification Statistical Modeling

What you'll do

Evaluate emerging vendors and data products to integrate non-traditional datasets into credit strategies.
Engineer high-impact features and build scalable processing and monitoring systems.
Build machine learning models through all phases of development, including design, training, and validation.
Serve as the dedicated data domain expert to guide users on dataset applications.
Navigate risk governance for vendor models and build challenger models using new data.
Use Python, AWS, H2O, and Spark to extract insights from large volumes of numeric and textual data.
Translate complex technical work into tangible business goals for stakeholders.

What we're looking for

Must have a Bachelor's in a quantitative field plus 6 years of data analytics experience, a Master's/MBA plus 4 years, or a PhD plus 1 year.
Must have at least 1 year of experience leveraging open source programming languages for large scale data analysis.
Must have at least 1 year of experience working with machine learning.
Must have at least 1 year of experience utilizing relational databases.
Must have experience with clustering, classification, sentiment analysis, time series, and deep learning.
Must have experience building, validating, and backtesting models and interpreting confusion matrices or ROC curves.
Must have experience with Python, Conda, AWS, H2O, Spark, and other cloud computing platforms.
PhD in a STEM field plus 3 years of experience in data analytics (preferred).
At least 1 year of experience working with AWS (preferred).
At least 4 years of experience in Python, Scala, or R for large scale data analysis (preferred).
At least 4 years of experience with machine learning (preferred).
At least 4 years of experience with SQL (preferred).

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