Manager, Data Scientist - Recommendation & Personalization Systems

Capital One Financial

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
McLean, VANew York, NYSan Jose, CA
Salary
$197,300–$225,100 / yr
Employment
Full-time
Posted
5 days ago
Freshness
Confirmed live yesterday
Nearby
99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $206k
This role $211k
$155k most similar roles pay here $258k

This role pays more than 54% of similar roles. Most pay $165,375–$247,500 — the shaded band above. At the midpoint, this role pays about $211k versus about $206k 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 1756 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 961 roles with salary data.

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View all roles at Capital One Financial

At a glance

TL;DR · Manager, Data Scientist - Recommendation & Personalization Systems

Role Posting Log in to save

Manager, Data Scientist - Recommendation & Personalization Systems joins an elite Applied AI team within AI Foundations to pioneer next-generation personalized customer experiences for web and mobile applications. The role involves architecting and deploying advanced recommendation engines by conducting original research into homegrown Foundation Models, Reinforcement Learning techniques, and Transformer-based architectures. You will collaborate with cross-functional teams of data scientists, software engineers, and product managers to build machine learning models through every phase of development, from design and training to evaluation and implementation. To uncover insights within massive volumes of numeric and textual data, you will utilize a technical stack including Python, Conda, AWS, H2O, Spark, and SQL. The work focuses on solving complex problems in Causal Inference and large-scale recommender systems to provide personalized interactions across billions of customer records.

Skills

Python SQL AWS Spark H2O Conda Scala R Machine Learning Deep Learning Reinforcement Learning Causal Inference Relational Databases Big Data Sentiment Analysis Time Series Clustering Classification

What you'll do

  • Architect and deploy high-scale personalized recommendation engines for web and mobile applications.
  • 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, and AWS to uncover actionable insights.
  • Conduct original research into Foundation Models, Reinforcement Learning, Causal Inference, and Transformer-based architectures.
  • Translate complex technical findings into tangible business goals for cross-functional stakeholders.
  • Manage the end-to-end lifecycle of data science solutions using open-source tools and cloud computing platforms.
  • Identify and solve large, undefined problems by applying state-of-the-art machine learning methods.

What we're looking for

  • A Bachelor's degree in a quantitative field plus 6 years of experience performing data analytics.
  • A Master's degree in a quantitative field or an MBA with a quantitative concentration plus 4 years of experience performing data analytics.
  • A PhD in a quantitative field plus 1 year of experience performing data analytics.
  • At least 1 year of experience leveraging open source programming languages for large scale data analysis.
  • At least 1 year of experience working with machine learning.
  • At least 1 year of experience utilizing relational databases.
  • A PhD in a STEM field plus 3 years of experience in data analytics (preferred).
  • At least 4 years of experience in Python, Scala, or R for large scale data analysis; 4 years with machine learning; and 4 years with SQL (preferred).

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