Manager, Data Scientist - Recommendation & Personalization Systems

Capital One Financial

Quick summary

Work type: On-site
Location: McLean, VANew York, NYSan Jose, CA
Salary: $197,300–$225,100 / yr
Posted: 7 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $169k

This role $211k

$115k most similar roles pay here $237k

This role pays more than 77% of similar roles. Most pay $126,800–$211,200 — the shaded band above. At the midpoint, this role pays about $211k versus about $169k 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 814 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 809 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

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As a Manager of Data Science within the Applied AI team at Capital One, you will lead the development and deployment of next-generation personalized recommendation engines that enhance customer experiences across web and mobile platforms. Your daily responsibilities include architecting scalable ML models using advanced techniques like Reinforcement Learning and Transformer-based architectures, while also conducting original research in areas such as Causal Inference to improve recommender systems. You will work with high-scale ML systems leveraging AWS, Kubeflow, and CI/CD pipelines, and utilize PyTorch for distributed training on multi-GPU setups. Additionally, you will handle petabyte-scale data processing using frameworks like DASK and PySpark to create sophisticated feature engineering solutions that drive personalized customer interactions at massive scale.

Skills

AWS Kubeflow CI/CD PyTorch DASK PySpark Transformer-based architectures MLOps multi-GPU optimization distributed training

What you'll do

Design and deploy scalable recommendation engines using advanced ML models.
Develop and optimize Transformer-based architectures for personalized experiences.
Implement MLOps practices to maintain production-grade ML systems at scale.
Conduct research on causal inference and reinforcement learning techniques.
Engineer petabyte-scale data processing pipelines with DASK and PySpark.

What we're looking for

At least 3 years of hands-on experience in building and maintaining high-scale ML systems.
Proficient in developing state-of-the-art Deep Learning models, especially Transformer-based architectures.
Expertise in distributed data processing for petabyte-scale feature engineering using frameworks like DASK and PySpark.
Experience with MLOps practices including AWS, Kubeflow, and CI/CD pipelines.
Strong skills in deploying ML systems with multi-GPU optimization using PyTorch.

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