Manager, Data Scientist - Credit Review
Capital One · Plano, TX
- On site
- Full-time
- $204,700 / year
- Plano, TX
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Job highlights
- Lead data science initiatives in credit risk.
- Build and challenge production machine learning models.
- Utilize Python, AWS, Spark, and other technologies.
- Collaborate with cross-functional teams on impactful solutions.
- Drive innovation in data-driven decision-making.
About the role
About Capital One's Data-Driven Culture
Data is at the center of everything we do. As a startup, we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and the relational database, cutting edge technology in 1988! Fast-forward a few years, and this little innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making.
As a Data Scientist at Capital One, you’ll be part of a team that’s leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives.
Team Description: Credit Review Models, Data, and Innovative Solutions
In Capital One’s Credit Review Models, Data and Innovative solutions team, we defend the company against model failures and find new ways of making better decisions with models. We use our statistics, software engineering, and business expertise to drive the best outcomes in both Risk Management and the Enterprise. We understand that we can’t prepare for tomorrow by focusing on today, so we invest in the future: investing in new skills, building better tools, and maintaining a network of trusted partners.
We partner with best-in-class data scientists, analysts, credit risk management experts, and engineers to innovate solutions that directly impact the company’s bottom line in a meaningful way. We do it all in a collaborative environment that values individual insight, encourages each associate to take on new responsibilities, promotes continuous learning, and rewards innovation.
Role Description
In this role, you will:
- Leverage a broad stack of technologies, such as, Python, Conda, AWS, H2O, Spark, and more, to reveal the insights hidden within huge volumes of numeric and textual data
- Build statistical and machine learning models to challenge the models in production
- Flex your interpersonal skills to translate the complexity of your work into tangible business goals
- Partner with a cross-functional team of data scientists, credit risk experts, and product managers to deliver a product customers love
The Ideal Candidate is:
- Technical: Comfortable with open-source languages and passionate about developing further. Hands-on experience developing data science solutions using open-source tools and cloud computing platforms.
- Statistically-minded: Built models, validated them, and backtested them. Knows how to interpret a confusion matrix or a ROC curve. Experience with clustering, classification, sentiment analysis, time series, and deep learning.
- Innovative: Continually researches and evaluates emerging technologies. Stays current on published state-of-the-art methods, technologies, and applications and seeks out opportunities to apply them.
- Creative: Thrives on bringing definition to big, undefined problems. Loves asking questions and pushing hard to find answers. Not afraid to share a new idea.
Basic Qualifications:
- Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date: A 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.
- A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 4 years of experience performing data analytics.
- A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related 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.
Preferred Qualifications:
- PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics.
- At least 4 years’ experience in Python, Scala, or R for large scale data analysis.
- At least 4 years’ experience with machine learning.
- At least 4 years’ experience with predictive modeling.
Key skills/competency
- Data Science
- Machine Learning
- Statistical Modeling
- Python
- AWS
- Spark
- Credit Risk
- Data Analysis
- Model Development
- Innovation
Skills & topics
- Data Scientist
- Machine Learning
- Statistical Modeling
- Python
- AWS
- Spark
- Credit Risk
- Data Analysis
- Model Development
- Innovation
- Manager
How to get hired
- Tailor your resume: Highlight experience with Python, machine learning, and statistical modeling relevant to credit risk.
- Showcase your technical skills: Emphasize experience with AWS, Spark, and other big data technologies in your application.
- Quantify your impact: Use numbers to demonstrate your successes in building and validating models.
- Prepare for technical interviews: Brush up on statistics, machine learning algorithms, and coding challenges.
- Understand Capital One's values: Research their commitment to data-driven innovation and customer impact.
Technical preparation
Practice Python for data analysis and ML.,Review statistical and machine learning concepts.,Familiarize yourself with AWS and Spark.,Prepare to discuss model validation techniques.
Behavioral questions
Describe a complex data problem you solved.,How do you translate technical work to business goals?,Share an example of innovation you drove.,How do you collaborate with cross-functional teams?
Frequently asked questions
- What is the role of a Data Scientist Manager in Capital One's Credit Review team?
- As a Data Scientist Manager in Capital One's Credit Review team, you will lead efforts to build and challenge statistical and machine learning models, leveraging technologies like Python, AWS, and Spark. You'll translate complex data insights into tangible business goals and collaborate with cross-functional teams to drive better decision-making in risk management.
- What technical skills are most important for this Data Scientist Manager role at Capital One?
- The ideal candidate for this Data Scientist Manager role at Capital One possesses strong technical skills in open-source languages like Python, experience with cloud platforms such as AWS, and a deep understanding of machine learning and statistical modeling. Proficiency with tools like Spark, H2O, and Conda is also highly valued.
- What kind of experience is required for the Data Scientist Manager position at Capital One?
- Capital One requires a Bachelor's degree in a quantitative field with 6 years of data analytics experience, or a Master's degree with 4 years, or a PhD with 1 year. Additionally, at least 1 year of experience with open-source programming languages for large-scale data analysis, machine learning, and relational databases is necessary.
- How does Capital One foster innovation within its Data Science teams?
- Capital One fosters innovation by encouraging continuous learning, investing in new skills and tools, and maintaining a network of trusted partners. The Credit Review Models, Data, and Innovative Solutions team specifically partners with data scientists, analysts, and engineers to develop groundbreaking solutions that impact the company's bottom line.
- What are the key responsibilities for a Data Scientist Manager in Credit Review at Capital One?
- Key responsibilities include leveraging technologies like Python and AWS to analyze large datasets, building and validating statistical and machine learning models to challenge existing ones, and collaborating with credit risk experts and product managers to deliver innovative solutions.
- What is the salary range for a Data Scientist Manager at Capital One in Charlotte, NC?
- For the Data Scientist Manager role in Charlotte, NC, the full-time annual salary range is $179,400 - $204,700, in addition to potential performance-based incentive compensation like bonuses and Long-Term Incentives (LTI).