Manager, Data Science

Capital One Financial

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
McLean, VA
Salary
$197,300–$225,100 / yr
Posted
3 days ago
Freshness
Confirmed live yesterday
Nearby
99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $178k
This role $211k
$128k most similar roles pay here $236k

This role pays more than 78% of similar roles. Most pay $143,975–$211,200 — the shaded band above. At the midpoint, this role pays about $211k versus about $178k 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 1020 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 1013 roles with salary data.

Most-posted roles

View all roles at Capital One Financial

At a glance

TL;DR · Manager, Data Science

Role Posting Log in to save

Manager, Data Science joins the Card DS team to innovate how the company handles data through analytics, infrastructure, valuations, and strategy. Working within a cross-functional team of data scientists, software engineers, and product managers, you will build machine learning models through all phases of development, including design, training, evaluation, validation, and implementation. You will process large volumes of numeric and textual data to solve complex business problems and provide actionable insights for the customer journey. The role requires proficiency in Python, Conda, AWS, H2O, Spark, PyTorch, DASK, and PySpark. Key technical competencies include experience with relational databases, deep learning, transformer-based architectures, clustering, classification, sentiment analysis, and time series analysis. You will utilize these tools to manage petabyte-scale data and develop production-grade systems using MLOps practices like Kubeflow and CI/CD pipelines.

Skills

Python AWS H2O Spark PyTorch Dask PySpark Conda Kubeflow CI/CD Machine Learning Deep Learning Relational Databases Sentiment Analysis Time Series Analysis Feature Engineering

What you'll do

  • Build machine learning models through all phases of development including design, training, evaluation, validation, and implementation.
  • Utilize a broad stack of technologies like Python, AWS, H2O, and Spark to uncover insights in large volumes of data.
  • Develop data science solutions from initial concept to production using open source tools and cloud platforms.
  • Process petabyte-scale datasets to perform feature engineering and extract relevant business insights.
  • Translate complex technical findings into tangible business goals for non-technical audiences.
  • Design, build, and maintain scalable systems to solve a variety of complex business problems.
  • Implement advanced techniques such as clustering, classification, sentiment analysis, time series analysis, and deep learning.

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.
  • PhD in a STEM field plus 3 years of experience in data analytics (preferred).
  • Experience building, deploying, and maintaining high-scale production ML systems using MLOps, AWS, Kubeflow, and CI/CD (preferred).
  • Expertise in developing and optimizing state-of-the-art Deep Learning models like Transformer-based architectures with PyTorch (preferred).
  • Extensive experience with high-performance, distributed data processing for petabyte-scale feature engineering using DASK and PySpark (preferred).

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