Senior Manager, Data Scientist - US Card (Generative AI Systems)

Senior Manager, Data Scientist - US Card (Generative AI Systems) on the Genesis team focuses on building and operating generative AI based data science solutions and ML driven products across the card business, with cross functional leadership and a broad tech stack.

Responsibilities

• Collaborate with a cross functional team of data scientists, software engineers, and product managers to deliver a product customers love
• Utilize a broad technology stack including Python, Kubernetes, AWS, H2O, Spark, and more to uncover insights from large volumes of numeric and textual data
• Develop machine learning models through all stages of the lifecycle from design through training, evaluation, validation, and deployment
• Translate the complexity of technical work into clear, actionable business objectives through strong interpersonal communication

Requirements

• Either a Bachelor's degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or related) with 7+ years of data analytics experience, or a Master's degree in a quantitative field with the degree expected by the start date
• At least 2 years of experience using open source programming languages for large scale data analysis
• At least 2 years of experience applying machine learning
• At least 2 years of experience working with relational databases

Technologies

• Python
• Kubernetes
• AWS
• H2O
• Spark

Benefits

• Health benefits
• Financial benefits (incentives, cash bonuses, long-term incentives)
• Inclusive set of health, financial and other benefits that support total well-being

Description

Data sits at the core of everything we do. Our early disruption of the credit card market came from personalizing offers through statistical modeling and relational databases, a move that helped launch a Fortune 200 company famed for data-driven decision making. As a Data Scientist on the Genesis team, you will join a group shaping the next wave of disruption at scale, working with cutting edge computing and ML technologies across billions of customer records to unlock opportunities that help people save time and money in their financial lives.

Team

The Generative AI Systems (Genesis) team within Card Data Science builds state-of-the-art generative AI solutions for dialogue, text summarization, reading comprehension, speech recognition, image and document processing, as well as time-series sequencing modeling. We partner with product, technology and design teams to deliver internal tools that improve efficiency for data analytics and business operations, as well as customer-facing applications that enhance the user experience.

Role Description

• 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, Kubernetes, 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

Qualifications

Basic qualifications

• Either a Bachelor's degree in a quantitative field with 7+ years of data analytics experience or a Master's degree in a quantitative field with the degree expected by the start date
• At least 2 years of experience using open source programming languages for large-scale data analysis
• At least 2 years of experience applying machine learning
• At least 2 years of experience working with relational databases

Preferred qualifications

• PhD in a STEM field plus 4 years of data analytics experience
• At least 1 year of AWS experience
• At least 1 year of people management experience
• At least 5 years of experience in Python, Scala, or R for large-scale data analysis
• At least 5 years of experience with machine learning

Type: Full-time