Manager, Data Scientist

Capital One; Chicago (IL); On-site; USD 179,000 - 205,000; Full time

Benefits offered by this job

Performance-based incentive

Comprehensive health benefits

Job summary

Capital One is seeking a Manager, Data Scientist in Chicago, IL to partner with cross-functional teams to identify, quantify, and govern model risks including GenAI-related risks. You will build, validate, and challenge models in production and contribute to governance across data science, software engineering, and product teams.

The ideal candidate has 6+ years analytics experience or a Master’s with relevant experience, strong open-source skills in Python/Scala/R, and hands-on ML with vector

Qualifications

• Bachelor’s degree in a quantitative field plus 6 years of experience performing data analytics, or Master’s degree in a quantitative field with the required experience.
• 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 or vector databases.

Responsibilities

• Collaborate with cross-functional teams to identify and quantify model risks.
• Apply a broad technology stack to uncover insights from large multi-modal data using frameworks and tools such as PyTorch, Hugging Face, LangChain, and vector databases, plus LLMOps and observability platforms.
• Develop machine learning models that challenge existing champion models in production and contribute to governance for the next generation of models.
• Validate diverse model types across multiple business domains and present risk impacts to executives.

Skills

Data analytics

Machine learning

Open-source languages

Relational databases

Vector databases

Education

Bachelor’s degree

Master’s degree

Tools

PyTorch

Hugging Face

LangChain

Vector databases

LLMOps

AWS

Python

Scala

R

Job description

Capital One’s Model Risk Office is seeking a Manager, Data Scientist to partner with cross-functional teams and help identify, quantify, and govern model risks that affect decision-making, including risks related to Generative AI.

Role Overview

In this onsite role in Chicago, IL, you will build, validate, and challenge models used in production. You will support model risk governance by working across data science, software engineering, and product teams to surface how model risks may impact business outcomes within the Enterprise Services division.

Key Responsibilities

• Collaborate with cross-functional teams of data scientists, software engineers, and product managers to identify and quantify risks associated with models.
• Apply a broad technology stack to uncover insights in large volumes of multi-modal data, using frameworks and tools such as PyTorch and Hugging Face, orchestration and retrieval components such as LangChain and vector databases, along with LLMOps and observability platforms.
• Develop machine learning models that challenge existing “champion models” currently deployed in production and contribute to the model governance framework for the next generation of models.
• Validate diverse model types across multiple business domains, and present to executives how identified model risks could affect the business.

Required Qualifications

• Currently has, or is in the process of obtaining (with expectation of completion on or before the scheduled start date), one of the following:
  • Bachelor’s degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 6 years of experience performing data analytics; or
  • Master’s degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, or related quantitative field) plus the required experience as outlined in the posting.
• 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 or vector databases.

Technologies and Tools

The role leverages PyTorch, Hugging Face, LangChain, Vector Databases, LLMOps, observability platforms, and additional tooling across cloud and programming ecosystems including AWS, Python, Scala, and R. It also references areas such as GenAI and model evaluation and analytics methods including confusion matrix and ROC curve, plus techniques such as clustering, classification, sentiment analysis, time series, and deep learning.

Team Description

The Capital One Model Risk Office focuses on safeguarding the company from model failures while improving decision-making through models, including unique risks associated with Generative AI (GenAI). The team applies expertise across statistics, software engineering, and business, and emphasizes continuous investment in future capabilities, tools, and partner relationships. Their approach also includes learning from past errors to develop more robust techniques to help prevent recurrence.

Benefits

• Eligible for performance-based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI).
• Comprehensive, competitive, and inclusive health, financial, and other benefits supporting overall well-being.

Ideal Candidate Profile

• Innovative: regularly research and evaluate emerging technologies and seek opportunities to apply state‑of‑the‑art methods.
• Creative: bring definition to complex problems and share new ideas while working through questions to find answers.
• Technical: comfortable with open‑source languages and hands‑on experience developing data science solutions using open‑source tools and cloud computing platforms.
• Statistically-minded: build, validate, and backtest models, with experience interpreting a confusion matrix and ROC curve, plus work across clustering, classification, sentiment analysis, time series, and deep learning.
• Data-focused: able to retrieve, combine, and analyze data from multiple sources and structures, recognizing that data understanding is often key to effective data science.

Preferred Qualifications

• PhD in a STEM field plus 3 years of experience in data analytics.
• At least 1 year of experience working with AWS.
• At least 4 years of experience in Python, Scala, or R for large scale data analysis.
• At least 4 years of experience with machine learning, including GenAI.
• At least 4 years of experience building or validating models related to fraud detection, digital marketing, cybersecurity, or sensitive data detection.

Salary and Application Details

• Chicago, IL: $179,400 - $204,700 (Mgr, Data Science).
• McLean, VA: $197,300 - $225,100 (Mgr, Data Science).
• Richmond, VA: $179,400 - $204,700 (Mgr, Data Science).
• Candidates hired to work in other locations will be subject to the pay range associated with that location.
• This role is expected to accept applications for a minimum of 5 business days.
• No agencies please.
• Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.