Senior Analyst - Data Analytics

American Express•New York, NY

About The Position

The Prospect Direct Mail Analytics team is part of the Analytics, Investments and Marketing Enablement (AIM) team within Global Commercial Services Marketing at American Express. AIM supports targeting, acquiring, engaging, and retaining commercial customers across online and offline channels while delivering analytics, insights, and data products for the Global Commercial Services business. The Senior Analyst independently leads complex, high-impact analytical initiatives supporting Commercial Prospect Direct Mail campaigns. The role focuses on profitable customer acquisition, revenue growth, and marketing investment efficiency. It develops advanced analytical solutions, identifies strategic growth opportunities, and translates business challenges into actionable recommendations using machine learning, statistical methods, and test-and-learn approaches. The position requires analytical judgment, technical expertise, ownership of end-to-end initiatives, and the ability to influence strategic decisions and communicate insights to senior stakeholders. The role works collaboratively with Marketing, Finance, Decision Science, Technology, and other Global Commercial Services teams to deliver measurable business impact.

Requirements

Bachelor's degree in a quantitative field such as Mathematics, Statistics, Computer Science, Physics, Engineering, Finance, or Economics.
Demonstrated experience independently leading complex analytical initiatives and influencing cross-functional teams to deliver measurable business outcomes.
Strong programming skills, including experience with big data technologies such as Hive or Spark and analytical programming languages such as Python, R, or Java.
Strong SQL skills and demonstrated ability to work with large, complex datasets to develop scalable analytical solutions.
Advanced skills in statistical analysis, predictive modeling, segmentation, optimization, and experimental design, including A/B testing.
Ability to independently structure ambiguous business problems, develop innovative solutions, and translate complex findings into actionable business recommendations.
Ability to communicate analytical insights and strategic recommendations clearly and compellingly to senior leaders and stakeholders.
Ability to work independently in a matrixed organization, manage competing priorities, and influence partners across Marketing, Finance, Decision Science, Risk, Technology, and external vendors.
Strong business acumen and ability to connect analytical solutions to acquisition growth, marketing efficiency, profitability, and revenue performance.

Nice To Haves

Master's degree in a quantitative field or an MBA with a strong quantitative background.
Strong knowledge of machine learning techniques, including XGBoost, Decision Trees, and Natural Language Processing (NLP) models.
Experience in marketing analytics, customer acquisition, campaign optimization, or investment optimization, preferably within financial services.
Experience evaluating machine learning model effectiveness and translating analytical insights into business strategies.
Knowledge of commercial data, prospect targeting, and external data sources.
Experience providing technical guidance, mentoring colleagues, or leading cross-functional analytical workstreams.

Responsibilities

Lead complex analytical initiatives to drive profitable acquisitions, optimize marketing investments, and improve ROI, acquisition, and revenue performance in the Direct Mail channel.
Define, design, develop, and implement analytical and data science solutions across the Direct Mail campaign lifecycle, from prospect identification and targeting through campaign execution and performance measurement.
Identify strategic growth opportunities and develop data-driven recommendations on channel strategy, investment allocation, prospect targeting, and offer personalization in partnership with GCS Prospect Marketing, Finance, and Investment Optimization teams.
Lead evaluation of new commercial data sources and work with external data vendors to assess data quality, incremental value, and opportunities to improve targeting effectiveness.
Develop and optimize prospect segmentation, targeting, and offer strategies using statistical modeling, machine learning, and advanced analytical techniques.
Translate ambiguous business challenges into structured analytical and machine learning problems; partner with Decision Science teams to evaluate model performance, quantify business impact, and identify model enhancements.
Collaborate with Machine Learning and Technology teams on scalable analytical solutions that improve targeting efficiency, campaign effectiveness, and operational performance.
Design and lead analytical experimentation, including A/B testing and performance measurement, to evaluate initiatives and improve marketing effectiveness.
Synthesize complex findings into clear recommendations and influence business decisions as an analytical partner to cross-functional stakeholders.
Provide analytical thought leadership and technical guidance to colleagues, promoting best practices, knowledge sharing, and continuous improvement.