JPMorgan Quant Analytics Senior Associate - Payments 

United States, Texas, Plano 

37383462

As a Quant Analytics Sr. Associate on the Commerce Enablement Analytics team, you’ll be supporting the Trust and Security Data work stream within the broader Data and Analytics organization. You will work closely with Product Owners, Data Owners, Tech and Data & Analytics across Consumer and Community Banking to deliver data strategy and analytics initiatives supporting Trust and Security.

Job Responsibilities:

• Deliver analytical approaches to identify opportunities to improve fraud and scam prevention efforts focused on protecting our customers while balancing customer satisfaction
• Develop and maintain comprehensive data strategy roadmap collaborating with Data Owners, Tech, Product owners aligned with Business goals and regulatory requirements
• Collaborate with stakeholders to understand business priorities and translate them to actionable data initiatives
• Collaborate with executive leadership to articulate vision, goals and roadmap for data driven decision-making across the organization
• Drive the adoption of best practices in data management and analytics within the organization within the organization through training, mentoring and knowledge sharing
• Continuously monitor and optimize data processes, analytical models, and infrastructure to drive continuous improvement and innovation
• Bring order to disparate requirements with high tolerance for ambiguity, very strong problem solving ability, and excellent client engagement skills
• Pivot quickly as client guidance evolves, always keeping the ultimate project objective in mind. Manage evolving project requirements while continuously learning quickly on-the-job
• Establish and embrace guidelines to ensure consistency and high quality of presentation materials in appearance, tone, and style
• Collaborate with unit managers, end users, developers, and other stakeholders to integrate data discoveries and processes into operational capabilities

Required qualifications, capabilities, and skills:

• 4+ years of industry experience in Data & Analytics
• Bachelor’s degree in relevant quantitative field required (e.g. Analytics, Statistics, Economics, Applied Math, Operations Research, Physics, Data Science fields);
• Ability to work collaboratively with cross-functional teams, including data scientists, analysts, and business stakeholders
• Experience working in Digital and/or product analytics and in depth understanding of common digital metrics and definitions
• Competency across a broad range of modern analytics tools (e.g., SQL, AWS, Hive, Hadoop, Spark, Python, R) and working on Digital behavioral data tools (E.g.: Adobe Analytics)
• Ability to work in large and medium sized project teams, as self-directed contributor with a proven track record of being detail orientated, innovative, creative, and strategic, with ability to influence and effectively collaborate with cross-functional teams
• Demonstrated ability to define business KPIs and establish measurement frameworks
• Structured thinker with passion for analyzing results and digging deeper and a strong aptitude for technical concepts and ideas
• Superior written and oral communication and presentation skills with experience communicating effectively with diverse audiences – across business and technology partners, including senior leadership
• Self-starter with out-of-the box problem solving skills and a drive to bring new ideas to life
• Strong time-management skills, with the ability to multi-task and keep numerous projects on track

Preferred qualifications, capabilities, and skills:

• Understanding of data governance principles and practices
• Cloud Platforms: Proficiency with cloud services from AWS, Google Cloud Platform, or Microsoft Azure
• Data Modeling: Strong understanding of data modeling concepts and techniques
• Data Warehousing: Knowledge of data warehousing solutions such as Amazon Redshift, Google BigQuery, or Snowflake.
• ETL Tools: Experience with ETL (Extract, Transform, Load) tools
• Basic knowledge in modern data mining, quantitative research, and data science techniques (e.g., decision trees, regressions, machine learning, string similarity, behavioral analytics, look-a-like models)
• Financial services background