Senior Director, Data Science - Head of Fair Lending Analytics - Fair & Responsible Banking Complian
Role details
Job location
Tech stack
Job description
The Compliance and Ethics Department is seeking a Data Scientist to lead the group of data scientists and analysts that help identify and mitigate fair lending and related compliance risk throughout Capital One. As the quantitative arm of the Fair & Responsible Banking Compliance Team (F&RB), the Fair Lending Analytics group partners with Legal department subject matter experts to conduct data analyses of lending decisions and provides guidance, advice and approvals for business area activities based on these analyses. This role involves leading experienced data scientists across all aspects of fair lending reviews and monitoring, including performing statistical data analyses, modeling on judgmental areas, working with business on the review of credit models and policies, and enabling the responsible development of AI in credit processes., In addition to being a great Data Scientist, this role requires a strong people leader responsible for overseeing a team of Compliance professionals, including other data scientists. This position reports to the Managing Vice President - F&RB Officer and Senior Compliance Officer Consumer Regulatory, and will join the F&RB leadership team.
In this role, you will:
- Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love
- Leverage a broad stack of technologies - Python, Conda, AWS, H2O, Spark, and more - to reveal the insights hidden within huge volumes of numeric and textual data
- Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
- Flex your interpersonal skills to translate the complexity of your work into tangible business goals and effectively mitigate compliance risks
- Develop and implement fair lending and responsible data use processes at Capital One to enable a winning credit business in an AI world
Requirements
- Innovative. You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them.
- A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You're passionate about talent development for your own team and beyond.
- Technical. You're comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms.
- Statistically-minded. You've built models, validated them, and backtested them. You know how to interpret a confusion matrix or a ROC curve. You have experience with clustering, classification, sentiment analysis, time series, and deep learning.
- A data guru. "Big data" doesn't faze you. You have the skills to retrieve, combine, and analyze data from a variety of sources and structures. You know understanding the data is often the key to great data science.
- Influential. You bring a proven track record of building relationships, inspiring trust, appropriately escalating issues in a timely way, and making grounded recommendations that appropriately balance risks and business needs., * 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 11 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 9 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 6 years of experience performing data analytics
- At least 6 years of experience leveraging open source programming languages for large scale data analysis
- At least 6 years of experience working with machine learning
- At least 6 years of experience utilizing relational databases, * PhD in "STEM" field (Science, Technology, Engineering, or Mathematics) plus 7 years of experience in data analytics
- At least 1 year of experience working with AWS
- At least 4 year of experience managing people
- At least 6 years of experience in Python, Scala, or R for large scale data analysis
- At least 7 years of experience with machine learning
Benefits & conditions
The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.
McLean, VA: $314,800 - $359,300 for Sr Dir, Data Science
Richmond, VA: $286,200 - $326,700 for Sr Dir, Data Science
Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter.
This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.
Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website (https://www.capitalonecareers.com/benefits) . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.