Manager, Data Science - Emerging ML

Capital One Financial

Confirmed live yesterday Low trust

Quick summary

Work type
On-site
Location
McLean, VANew York, NYSan Jose, CA
Salary
$197,300–$225,100 / yr
Posted
73 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $226k
This role $211k
$175k most similar roles pay here $277k

This role pays less than 61% of similar roles. Most pay $193,935–$258,378 — the shaded band above. At the midpoint, this role pays about $211k versus about $226k 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 998 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 992 roles with salary data.

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View all roles at Capital One Financial

At a glance

TL;DR · Manager, Data Science - Emerging ML

Manager, Data Science - Emerging ML joins the Applied Research organization to focus on research and development of artificial intelligence technologies, specifically embeddings and foundation models. This individual contributor role involves conducting research into self-supervised learning, transformer models, and representation learning while building customer behavioral models using transaction and clickstream data. The successful candidate will manage the full data science lifecycle, from designing and training models to partnering with engineering teams to operationalize them in production systems for marketing, servicing, and fraud prevention. Key responsibilities include writing software to analyze numerical and textual data at scale. Required technical skills include Python, Scala, R, SQL, Spark, and AWS. The role requires expertise in clustering, classification, sentiment analysis, time series analysis, and deep learning to solve complex problems involving billions of customer records and large-scale datasets.

What you'll do

  • Conduct research into self-supervised learning, transformer models, and representation learning.
  • Build customer behavioral models using transaction and clickstream data to identify trends and patterns.
  • Develop machine learning models through all phases of development including design, training, evaluation, and validation.
  • Partner with engineering teams to operationalize models in scalable production systems for millions of customers.
  • Write Python or Scala code to collect, explore, and analyze large-scale numerical and textual data using Spark and AWS.
  • Execute experiments with product teams to improve customer experiences in marketing, servicing, and fraud prevention.
  • Refine integration patterns for encoder and decoder models to connect research products with business use cases.

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.
  • Experience developing data science solutions using open source tools and modern cloud computing platforms like AWS.
  • Proficiency in Python, Scala, or R to analyze numerical and textual data.

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