Manager, Data Science - Emerging ML

Emerging ML is the data science and machine learning team inside Capital One's Applied Research organization. We focus on research and development of new technologies within the domain of Artificial Intelligence with a focus on Embeddings and Foundation Models. We partner closely with our product and engineering teams to connect emerging technologies with business critical use cases across Capital One's lines of business.

As part of Emerging ML, you will work on things like:

• Conducting research into self supervised learning, transformer models, and representation learning
• Building customer behavioral models (using transaction, clickstream, and other data) that identify trends, patterns, and relationships related to product usage
• Refining integration patterns for encoder and decoder models for downstream use cases to connect Applied Research products and business use cases

Role Description

This is an individual contributor position. In Emerging ML, you will work at all phases of the data science lifecycle, including:

• Build machine learning models through all phases of development, from design through training, evaluation and validation, and partner with engineering teams to operationalize them in scalable and resilient production systems that serve 50+ million customers.
• Partner closely with a variety of business and product teams across Capital One to conduct the experiments that guide improvements to customer experiences and business outcomes in domains like marketing, servicing and fraud prevention.
• Write software (Python, Scala, e.g.) to collect, explore, visualize and analyze numerical and textual data (billions of customer transactions, clicks, payments, etc.) using tools like Spark and AWS.

• Curious and creative. You thrive on bringing definition to big, undefined problems. You love asking questions, and you love pushing hard to find the answers. You're not afraid to share a new idea. You communicate clearly and effectively to share your findings with non-technical audiences.
• Technical: You have hands-on experience developing data science solutions from concept to production using open source tools and modern cloud computing platforms. You are not afraid of petabytes of data.
• Statistically-minded. You have 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 analysis and deep learning.
• Customer and product oriented. You share our passion for changing banking for good., * 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, * PhD in "STEM" field (Science, Technology, Engineering, or Mathematics)
• Experience working with AWS
• At least 4 years' experience in Python, Scala, or R
• At least 4 years' experience with machine learning
• At least 4 years' experience with SQL

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., Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. 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: $197,300 - $225,100 for Mgr, Data Science New York, NY: $215,200 - $245,600 for Mgr, Data Science San Jose, CA: $215,200 - $245,600 for Mgr, 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. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level., Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).