Manager-Data Science

About the role

Manager-Data Science New York, NY, United States (Hybrid) Apply Now JOB DESCRIPTION The AIM (Analytics, Investment & Marketing Enablement) team – a part of GCS Marketing – is the analytical engine that enables the Global Commercial Services portfolio of American Express. Accelerating growth momentum, increasing profitability, and strengthening our value proposition are key objectives for this organization. As a Manager (B35), you will lead analytical initiatives that power the next generation of American Express' Corporate customer acquisition engine across Sales & Marketing. You will combine advanced analytics, machine learning, and emerging Generative AI capabilities to solve complex business problems, improve customer targeting, personalize engagement, and drive measurable acquisition outcomes. You will own analytical solutions from problem definition through deployment, partnering closely with Marketing, Sales, Product, Digital, and Technology teams to build scalable, production-ready data products that influence strategic business decisions. This role is ideal for someone who enjoys solving ambiguous business problems, remains technically hands-on, and is excited to shape the future of AI-driven customer acquisition. RESPONSIBILITIES Translate complex business problems into structured analytical frameworks and develop data-driven solutions that improve targeting, prospect identification, personalization, and acquisition performance. Design, build, and deploy predictive models, machine learning solutions, customer scoring frameworks, prioritization models, and AI-powered analytical products that deliver measurable business impact. Develop scalable analytical pipelines using Python, SQL, and modern AI/ML techniques with a strong focus on production readiness, automation, and reusability. Apply Generative AI techniques - including Large Language Models (LLMs), Retrieval Augmented Generation (RAG), embeddings, prompt engineering, and NLP - to enhance customer acquisition, lead qualification, personalization, and decision intelligence. Evaluate and select appropriate analytical and GenAI approaches by balancing model accuracy, explainability, scalability, latency, cost efficiency, and business value. Partner closely with Marketing, Sales, Product, Engineering, and Technology teams to define analytical use cases, integrate solutions into business workflows, and accelerate adoption. Lead experimentation and optimization by designing A/B tests, measuring business impact, monitoring model performance, and continuously refining analytical solutions. Ensure analytical models and AI solutions adhere to Responsible AI principles, including model governance, explainability, bias assessment, data quality, monitoring, and regulatory compliance. Present analytical findings, business recommendations, and AI solution strategies to senior leadership with clear storytelling and actionable insights. Stay current with emerging AI, machine learning, and Generative AI technologies, identifying opportunities to improve customer acquisition capabilities across the Commercial business. QUALIFICATIONS Minimum Qualifications Bachelor's degree in Engineering, Computer Science, Statistics, Mathematics, Economics, Finance, or another quantitative discipline. 2+ years of experience in Data Science, Advanced Analytics, Machine Learning, Decision Science, or Customer Analytics. Strong hands-on experience with Python and SQL, building end-to-end analytical solutions on large-scale datasets. Experience developing predictive models, machine learning solutions, and customer targeting or decisioning frameworks that drive measurable business outcomes. Working knowledge of Generative AI concepts and applications, including LLMs, prompt engineering, RAG, or NLP techniques. Proven ability to translate complex business problems into scalable analytical solutions and influence decisions through data-driven insights. Experience collaborating with cross-functional teams across business, product, engineering, and technology in a fast-paced environment. Excellent communication, stakeholder management, and executive presentation skills. Preferred Qualifications Experience developing production-grade AI/ML or Generative AI applications. Experience with experimentation frameworks, A/B testing, causal measurement, and model performance monitoring. Familiarity with Responsible AI, model governance, explainability, fairness, and AI risk management. Experience collaborating with Product, Engineering, and Marketing teams to operationalize analytical solutions. 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. At American Express, our culture is built on a 175-year history of innovation, shared values and Leadership Behaviors, and an unwavering commitment to back our customers, communities, and colleagues. From delivering differentiated products to providing world-class customer service, we operate with a strong risk mindset, ensuring we continue to uphold our brand promise of trust, security, and service. As part of Team Amex, you’ll experience our powerful backing with comprehensive support for your holistic well-being and many opportunities to learn new skills, develop as a leader, and grow your career. Here, your voice and ideas matter, your work makes an impact, and together, you will help us define the future of American Express. We back you with benefits that support your holistic well-being so you can be and deliver your best. This means caring for you and your loved ones' physical, financial, and mental health, as well as providing the flexibility you need to thrive personally and professionally: Competitive base salaries Bonus incentives 6% Company Match on retirement savings plan Free financial coaching and financial well-being support Comprehensive medical, dental, vision,...

Key responsibilities

• check_circleCollaborate with the team on day-to-day project tasks
• check_circleLearn tools and processes used by the organization
• check_circleDocument work and participate in team meetings
• check_circleSupport quality checks and continuous improvement

Requirements

• check_circleTranslate complex business problems into structured analytical frameworks and develop data-driven solutions that improve targeting, prospect identification, personalization, and acquisition performance.
• check_circleDesign, build, and deploy predictive models, machine learning solutions, customer scoring frameworks, prioritization models, and AI-powered analytical products that deliver measurable business impact.
• check_circleDevelop scalable analytical pipelines using Python, SQL, and modern AI/ML techniques with a strong focus on production readiness, automation, and reusability.
• check_circleApply Generative AI techniques - including Large Language Models (LLMs), Retrieval Augmented Generation (RAG), embeddings, prompt engineering, and NLP - to enhance customer acquisition, lead qualification, personalization, and decision intelligence.
• check_circleEvaluate and select appropriate analytical and GenAI approaches by balancing model accuracy, explainability, scalability, latency, cost efficiency, and business value.
• check_circlePartner closely with Marketing, Sales, Product, Engineering, and Technology teams to define analytical use cases, integrate solutions into business workflows, and accelerate adoption.
• check_circleLead experimentation and optimization by designing A/B tests, measuring business impact, monitoring model performance, and continuously refining analytical solutions.
• check_circleEnsure analytical models and AI solutions adhere to Responsible AI principles, including model governance, explainability, bias assessment, data quality, monitoring, and regulatory compliance.
• check_circlePresent analytical findings, business recommendations, and AI solution strategies to senior leadership with clear storytelling and actionable insights.
• check_circleStay current with emerging AI, machine learning, and Generative AI technologies, identifying opportunities to improve customer acquisition capabilities across the Commercial business.
• check_circleBachelor's degree in Engineering, Computer Science, Statistics, Mathematics, Economics, Finance, or another quantitative discipline.
• check_circle2+ years of experience in Data Science, Advanced Analytics, Machine Learning, Decision Science, or Customer Analytics.

Skills & keywords

Benefits & perks

• check_circleMentorship
• check_circleCertificate of completion
• check_circleFlexible work arrangement where applicable