Manager, Data Science

Capital One Financial

Confirmed live yesterday Low trust

Quick summary

Work type: On-site
Location: McLean, VA
Employment: Full-time
Posted: 53 days ago
Freshness: Confirmed live yesterday
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

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Manager, Data Science joins the Card DS team to innovate how the business handles data through analytics, infrastructure, and strategy. You will partner with a cross-functional team of data scientists, software engineers, and product managers to deliver products by building machine learning models through all phases of development, including design, training, evaluation, validation, and implementation. You will leverage a broad stack of technologies including Python, Conda, AWS, H2O, Spark, PyTorch, and DASK to reveal insights within huge volumes of numeric and textual data. The role involves solving complex business problems by extracting relevant insights from petabytes of data using techniques like clustering, classification, sentiment analysis, time series analysis, and deep learning to support large-scale data and cloud-based processing.

What does a Data Science Manager earn?

Median $211200 from 46 postings across 11 companies.

See salary data

Skills

Python AWS Spark PySpark H2O Conda PyTorch Kubeflow DASK CI/CD MLOps Relational Databases Deep Learning 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 implementation.
Leverage a broad technology stack including Python, AWS, H2O, and Spark to extract insights from large-scale data.
Develop data science solutions from initial concept through to production using open-source tools and cloud platforms.
Perform feature engineering and data processing on petabyte-scale numeric and textual datasets.
Translate complex technical work and findings into tangible business goals for non-technical audiences.
Partner with cross-functional teams of engineers and product managers to deliver customer-facing products.
Validate and backtest models using statistical techniques such as clustering, classification, and time series analysis.

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 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.
PhD in a STEM field plus 3 years of experience in data analytics (preferred).
3+ years of experience building and maintaining production-grade ML systems using MLOps, AWS, Kubeflow, and CI/CD (preferred).

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