Manager, Data Science

Capital One Financial

Confirmed live today Low trust

Quick summary

Work type: On-site
Location: McLean, VA
Employment: Full-time
Posted: 52 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $190k

$134k $235k

below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $165,000–$214,900.

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 2276 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 1147 roles with salary data.

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At a glance

TL;DR · Manager, Data Science

Role Posting Log in to save

JOB TITLE: Manager, Data Science The Manager, Data Science joins the Card DS team to innovate data handling through analytics, infrastructure, valuations, and strategy. This role involves partnering with a cross-functional team of data scientists, software engineers, and product managers to deliver products that improve the customer journey. You will build machine learning models through all phases of development, from design and training to evaluation, validation, and implementation. You will leverage a broad technology stack including Python, Conda, AWS, H2O, Spark, PyTorch, and DASK to extract insights from petabytes of numeric and textual data. Key responsibilities include performing clustering, classification, sentiment analysis, and time series analysis. You will solve complex business problems by translating technical complexity into tangible goals using relational databases and modern cloud computing platforms.

What does a Data Science Manager earn?

Median $211200 from 46 postings across 11 companies.

See salary data

Skills

Python AWS Spark PySpark H2O PyTorch Kubeflow Conda DASK CI/CD MLOps Deep Learning Relational Databases Transformer-based architectures Time Series Analysis Sentiment Analysis Clustering Classification

What you'll do

Build machine learning models through all phases of development including design, training, evaluation, and validation.
Leverage a broad technology stack including Python, AWS, H2O, and Spark to extract insights from large datasets.
Develop data science solutions from initial concept through to production using open source tools and cloud platforms.
Perform high-performance, distributed data processing for petabyte-scale feature engineering using frameworks like DASK and PySpark.
Optimize and deploy state-of-the-art Deep Learning models, specifically Transformer-based architectures, using PyTorch.
Translate complex technical work and findings into tangible business goals for non-technical audiences.
Maintain high-scale, production-grade ML systems using MLOps practices, including Kubeflow and CI/CD pipelines.

What we're looking for

Must have a Bachelor's in a quantitative field plus 6 years of data analytics experience.
Must have a Master's in a quantitative field or an MBA with a quantitative concentration plus 4 years of data analytics experience.
Must have a PhD in a quantitative field plus 1 year of data analytics experience.
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 hands-on experience developing data science solutions from concept to production using open source tools and cloud platforms.
Must have experience with clustering, classification, sentiment analysis, time series analysis, and deep learning.

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