Senior Associate, Data Scientist - Model Risk Office
$123k - $141k • McLean, VA • Full-time
Posted 25d ago
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
The Capital One Model Risk Office is dedicated to safeguarding the company from model failures while simultaneously enhancing decision-making through models, including unique risks associated with Generative AI (GenAI). Leveraging expertise in statistics, software engineering, and business, we strive to achieve optimal results for both Risk Management and the broader Enterprise. We prioritize long-term success by continually investing in future capabilities: acquiring new skills, developing superior tools, and cultivating strong relationships with trusted partners. Our approach involves learning from past errors to develop increasingly robust techniques that prevent recurrence.
In this role, you will partner with a cross-functional team of data scientists, software engineers, and product managers to identify and quantify risks associated with models. You will leverage a broad stack of technologies to reveal insights hidden within huge volumes of multi-modal data, build machine learning models to challenge existing production models, and contribute to the model governance framework for the next generation of models. You will also validate a wide variety of models across multiple business domains and present identified model risks to executives.
Responsibilities
- Partner with cross-functional teams to identify and quantify model risks.
- Leverage a broad stack of technologies to reveal insights from multi-modal data.
- Build machine learning models to challenge champion models.
- Contribute to the model governance framework for new models.
- Validate a wide variety of models across multiple business domains.
- Present identified model risks and their business impact to executives.
Requirements
- Bachelor's Degree in a quantitative field plus 2 years of data analytics experience, or a Master's Degree in a quantitative field or an MBA with a quantitative concentration.
- Experience researching and evaluating emerging technologies and state-of-the-art methods.
- Experience developing data science solutions using open-source tools and cloud computing platforms.
- Experience building, validating, and backtesting models, including interpreting statistical metrics.
- Experience with clustering, classification, sentiment analysis, time series, and deep learning.
- Skills to retrieve, combine, and analyze data from various sources and structures.
- Master’s Degree or PhD in a STEM field (preferred).
- Experience working with AWS (preferred).
- At least 2 years’ experience in Python, Scala, or R for large scale data analysis (preferred).
- At least 2 years’ experience with machine learning (preferred).
Benefits
- Comprehensive, competitive, and inclusive set of health, financial and other benefits.
- Performance-based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI).