Senior Analyst - Data Analytics

The ‘Prospect Direct Mail Analytics’ team is part of the Analytics, Investments and Marketing Enablement (AIM) team within Global Commercial Services Marketing, American Express. The AIM team is responsible for targeting, acquiring, engaging, and retaining commercial customers across online and offline channels while delivering world-class analytics, insights, and data products for the Global Commercial Services (GCS) business. In this role, the incumbent will play a key role in shaping analytical strategies, leading complex initiatives, and driving business outcomes for the Prospect Direct Mail channel within AIM.

Purpose of the Role: The Senior Analyst will independently lead complex, high-impact analytical initiatives supporting Commercial Prospect Direct Mail campaigns, with a focus on driving profitable customer acquisition, revenue growth, and marketing investment efficiency. The incumbent will be responsible for developing advanced analytical solutions, identifying strategic growth opportunities, and translating complex business challenges into actionable recommendations through machine learning, statistical methodologies, and disciplined test-and-learn approaches. The role requires strong analytical judgment, technical expertise, and the ability to influence strategic decision-making across cross-functional partners. The ideal candidate will demonstrate ownership of end-to-end analytical initiatives, proactively identify opportunities to enhance targeting and campaign performance, and effectively communicate insights to senior stakeholders. This position operates in a highly collaborative environment, partnering with Marketing, Finance, Decision Science, Technology, and other teams across Global Commercial Services to deliver measurable business impact.

• Independently lead complex analytical initiatives to drive profitable acquisitions, optimize marketing investments, and improve return on investment (ROI), acquisition, and revenue performance within the Direct Mail channel.
• Define, design, develop, and implement advanced analytical and data science solutions across the end-to-end Direct Mail campaign lifecycle, from prospect identification and targeting through campaign execution and performance measurement.
• Identify strategic growth opportunities and develop data-driven recommendations to influence channel strategy, investment allocation, prospect targeting, and offer personalization in partnership with GCS Prospect Marketing, Finance, and Investment Optimization teams.
• Lead the evaluation of new commercial data sources and collaborate with external data vendors to assess data quality, incremental value, and opportunities to enhance targeting effectiveness.
• Develop and optimize sophisticated prospect segmentation, targeting, and offer strategies by leveraging statistical modeling, machine learning, and advanced analytical techniques to maximize acquisition profitability and customer value.
• Translate complex and ambiguous business challenges into structured analytical and machine learning problems, partnering with Decision Science teams to evaluate model performance, quantify business impact, and identify opportunities for model enhancement.
• Collaborate with Machine Learning and Technology teams to guide the development and implementation of 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 strategic initiatives, generate actionable insights, and continuously improve marketing effectiveness.
• Serve as a trusted analytical partner to cross-functional stakeholders, synthesizing complex findings into clear, actionable recommendations and influencing business decisions through data-driven insights.
• Provide analytical thought leadership and technical guidance to colleagues, promoting best practices, knowledge sharing, and continuous improvement in analytical methodologies and processes.

Minimum Qualifications:

• Bachelor's degree in a quantitative field (e.g., 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 with experience in big data technologies (e.g., Hive, 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 technical and analytical skills, including statistical analysis, predictive modeling, segmentation, optimization, and experimental design (e.g., A/B testing).
• Strong analytical and conceptual thinking skills, with demonstrated ability to independently structure ambiguous business problems, develop innovative solutions, and translate complex findings into actionable business recommendations.
• Proven ability to communicate analytical insights and strategic recommendations to senior leaders and stakeholders in a clear, concise, and compelling manner.
• Demonstrated ability to work independently in a matrixed organization, manage competing priorities, and influence cross-functional partners across Marketing, Finance, Decision Science, Risk, Technology, and external vendors.
• Strong business acumen with the ability to connect analytical solutions to measurable outcomes, including acquisition growth, marketing efficiency, profitability, and revenue performance.

Preferred Qualifications:

• Master's degree in a quantitative field (e.g., Mathematics, Statistics, Computer Science, Physics, Engineering, Finance, or Economics) or 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 is a plus.
• Experience providing technical guidance, mentoring colleagues, or leading cross-functional analytical workstreams.

Depending on factors such as business unit requirements, the nature of the position, cost and applicable laws, American Express may provide visa sponsorship for certain positions.