Performance Marketing Analytics - Senior Associate

JPMorgan Chase & Co.; Columbus (OH); On-site; USD 95,000 - 150,000; Full time

Job summary

JPMorgan Chase & Co. in Columbus, OH, is seeking a Quantitative Analytics Senior Associate within Performance Marketing Analytics to own the lifecycle from raw data to executive-ready insight.

You will design experiments, build data pipelines, and partner with marketing, product, and technology stakeholders to measure performance and optimize customer acquisition. The role emphasizes clear storytelling with data and responsible use of AI-assisted analytics tools.

Qualifications

• Bachelor’s degree in Data Science, Computer Science, or a related technical field.
• 2+ years of applied experience in analytics, software engineering, or a related field.
• Hands‑on experience with system design, application development, testing, and operational stability.
• Strong SQL skills and experience working with relational databases to extract, manipulate, and analyze data.
• Experience building analytical solutions across multiple data sources, with attention to data quality and reproducibility.
• Ability to manage multiple priorities independently with strong organization and attention to detail.
• Strong written and verbal communication skills, with the ability to distill complex findings into clear, concise insights for varied audiences.
• Experience translating analytical outcomes into business actions in partnership with cross‑functional stakeholders.

Responsibilities

• Identify, integrate, and analyze large, complex datasets from multiple sources to generate actionable insights and recommendations
• Design measurement approaches and experiments (including A/B, multivariate, and holdout designs) to quantify impact and inform decisions
• Interpret experimental results using primary and guardrail metrics, statistical significance, and practical business impact
• Diagnose performance drivers through segmentation, funnel analysis, and anomaly detection (for example: device, customer type, drop‑off points, traffic allocation, and logging gaps)
• Build and maintain scalable, production‑ready data pipelines and analytical frameworks that enable repeatable reporting and insights
• Write secure, stable, testable, and maintainable code using SQL and Python or R, with strong attention to quality and controls
• Communicate complex findings through clear narratives and visualizations; present to senior stakeholders and influence roadmaps and priorities
• Stay current on innovations in analytics and responsibly use approved AI‑assisted analytics tools to accelerate development, documentation, and issue triage

Skills

SQL

Python

Data analysis

Stakeholder communication

Education

Bachelor's degree in Data Science/CS

Master's degree (preferred)

Tools

Tableau

Alteryx

Job description

Join a team that helps shape how millions of customers discover and choose Chase digital experiences. You will work at the intersection of analytics, data science, and data engineering to turn complex data into clear decisions and scalable solutions.

As a Quantitative Analytics Senior Associate at JPMorganChase within Performance Marketing Analytics, you will own the lifecycle from raw data to executive-ready insight. You will design and interpret experiments, develop analytical frameworks, and build reliable pipelines that help teams move faster with confidence. You will partner with marketing, product, and technology stakeholders to measure performance, optimize customer acquisition, and strengthen Owned Media strategy through data.

Job responsibilities

• Identify, integrate, and analyze large, complex datasets from multiple sources to generate actionable insights and recommendations
• Design measurement approaches and experiments (including A/B, multivariate, and holdout designs) to quantify impact and inform decisions
• Interpret experimental results using primary and guardrail metrics, statistical significance, and practical business impact
• Diagnose performance drivers through segmentation, funnel analysis, and anomaly detection (for example: device, customer type, drop‑off points, traffic allocation, and logging gaps)
• Build and maintain scalable, production‑ready data pipelines and analytical frameworks that enable repeatable reporting and insights
• Write secure, stable, testable, and maintainable code using SQL and Python or R, with strong attention to quality and controls
• Communicate complex findings through clear narratives and visualizations; present to senior stakeholders and influence roadmaps and priorities
• Stay current on innovations in analytics and responsibly use approved AI‑assisted analytics tools to accelerate development, documentation, and issue triage

Required qualifications, capabilities and skills

• Bachelor’s degree in Data Science, Computer Science, or a related technical field
• 2+ years of applied experience in analytics, software engineering, or a related field
• Hands‑on experience with system design, application development, testing, and operational stability
• Strong SQL skills and experience working with relational databases to extract, manipulate, and analyze data
• Experience building analytical solutions across multiple data sources, with attention to data quality and reproducibility
• Ability to manage multiple priorities independently with strong organization and attention to detail
• Strong written and verbal communication skills, with the ability to distill complex findings into clear, concise insights for varied audiences
• Experience translating analytical outcomes into business actions in partnership with cross‑functional stakeholders

Preferred qualifications, capabilities and skills

• Master’s degree in Data Science, Computer Science, or a related technical field
• Experience with data visualization or workflow tools (for example: Tableau or Alteryx)
• Proficiency in Python or R for analysis and automation
• Experience applying machine learning and generative AI tools to automate workflows and deliver insights, with a responsible and compliant approach
• Understanding of responsible AI practices, including data sensitivity considerations and secure handling of inputs and outputs