Senior Analyst Data Analytics – New York Hybrid | Visa + Salary Insights at American Express

📍 Location: us

🏷 Type: Full-time

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Job Overview

The Senior Analyst – Data Analytics role at American Express sits within the AIM (Analytics, Investment & Marketing Enablement) team under Global Commercial Services. This position focuses on building advanced data science, machine learning, and Generative AI-driven solutions that improve targeting, acquisition, and engagement for commercial clients. The ideal candidate is a quantitatively strong analyst with hands-on experience in Python, SQL, and predictive modeling. You will work closely with decision science teams, external data vendors, and marketing stakeholders to design scalable data products that directly impact revenue growth and business optimization.

📅 Job Timeline & Status

This role is currently in an active hiring phase with a clearly defined application window. Given the short timeline and competitive brand positioning, candidates should apply immediately. The job is likely in a mid-cycle recruitment stage where screening and shortlisting begin soon after the deadline, meaning early applicants may receive higher visibility.

🌍 Work Eligibility & Location

This is a Hybrid role based in New York, requiring periodic office presence. While visa sponsorship may be available, it is typically reserved for highly qualified candidates with strong machine learning or enterprise analytics experience. The role is best suited for professionals already authorized to work in the US or those with strong technical differentiation.

💰 Salary Intelligence

The compensation is highly competitive for a Mid-Level analytics role, especially when factoring in bonus incentives, retirement matching, and comprehensive benefits. Total compensation can significantly exceed base salary depending on performance and business impact.

📊 Role Breakdown

This role centers on designing and deploying advanced analytics systems that improve commercial decision-making across American Express. Approximately 40% of the role focuses on building machine learning models for targeting and segmentation, while 30% involves data enrichment and quality improvement using external vendor data. Another 20% is dedicated to developing scalable AI/ML pipelines and Generative AI workflows for marketing optimization. The remaining 10% focuses on stakeholder communication, model governance, and production monitoring.

You will be expected to translate business problems into structured analytical frameworks using Python and SQL, build predictive models for propensity scoring, and deploy production-ready solutions that improve targeting efficiency. A key responsibility includes working with external data vendors to validate and integrate third-party datasets for improved commercial insights. The role also requires designing LLM-based workflows for tasks such as lead scoring, customer segmentation, and engagement optimization. Strong communication is essential for aligning cross-functional stakeholders and ensuring analytical outputs directly influence business strategy.

🧩 Required Skills & Fit

📈 Difficulty & Competitiveness

This is a High Difficulty role due to the combination of enterprise-scale analytics, machine learning, and emerging GenAI requirements. Candidates with production-level ML deployment experience and strong business communication skills will have a clear advantage.

🚀 Career Impact

This role provides strong career acceleration within enterprise AI systems and financial analytics. Exposure to large-scale data products, production ML systems, and Generative AI pipelines significantly enhances long-term positioning for senior data science and AI leadership roles.

📋 Key Responsibilities

You will design and implement machine learning models to improve customer targeting and engagement. Responsibilities include working with external vendors to integrate third-party datasets, improving data quality systems, and building scalable AI-driven marketing solutions. You will also develop LLM-based workflows to support decision automation and personalization across commercial channels. Additional duties include optimizing predictive models, deploying analytics pipelines into production, and continuously monitoring model performance. Collaboration with cross-functional teams is essential to ensure insights translate into measurable business impact.

🎯 Application Strategy

🧠 Application Optimization (Adaptive)

This role requires strong alignment between technical depth and business impact storytelling.

🧠 Fit & Positioning Analysis

Evaluate your match before applying.

📅 Application Signals

Hiring urgency is high due to the tight deadline of 2026-06-04. Competition is expected to be strong given the brand strength of American Express and the growing demand for AI-driven analytics roles. Candidates should expect a fast initial screening cycle, with interviews likely starting shortly after the application window closes. Because this role integrates machine learning, Generative AI, and commercial analytics, applicant volume will be global and highly competitive. Strong applicants should apply immediately to maximize visibility.

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✅ Job Source & Verification

This job listing is sourced directly from the official American Express careers portal, ensuring high source credibility. Details such as salary range, responsibilities, and timeline were verified against the posting dated 2026-05-28 and last checked on 2026-05-31. Information reflects an actively maintained listing with reliable corporate hiring signals and structured role documentation typical of enterprise-level data science positions.