Model Risk Data Science Lead

Capital One; Chicago (IL); On-site; USD 140,000 - 200,000; Full time

Benefits offered by this job

Comprehensive health benefits

Competitive salary

Inclusive benefits supporting well-being

Job summary

A leading financial institution is seeking a skilled candidate to join their data science team in Chicago. This role involves partnering with various teams to identify risks and build machine learning models. Candidates should possess a quantitative degree and extensive experience in data analysis, open-source programming, and machine learning practices. The position offers a competitive benefits package and supports diverse well-being initiatives, making it appealing to potential applicants.

Qualifications

• At least 1 year of experience leveraging open source programming languages for large scale data analysis.
• At least 4 years’ experience with machine learning, including GenAI.
• Experience in building or validating models related to fraud detection or digital marketing.

Responsibilities

• Partner with cross-functional teams to identify and quantify risks.
• Build machine learning models for production.
• Validate models across multiple business domains.

Skills

Open source programming languages

Machine learning

Data retrieval and analysis

Statistical analysis

Interpersonal skills

Education

Bachelor's Degree in a quantitative field

Master's Degree or MBA in a quantitative concentration

PhD in a quantitative field

Tools

Python

AWS

Relational databases

Vector databases

PyTorch

Job description

A leading financial institution is seeking a skilled candidate to join their data science team in Chicago. This role involves partnering with various teams to identify risks and build machine learning models. Candidates should possess a quantitative degree and extensive experience in data analysis, open-source programming, and machine learning practices. The position offers a competitive benefits package and supports diverse well-being initiatives, making it appealing to potential applicants.