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Salary
$81k – $160k per year (Estimated)
Location
In office (New York)
Seniority
Middle · 4+ years exp

Confirmed on the employer's own hiring board on Sep 23, 2026. First seen by Alion on Sep 23, 2026. American Express scores A on the Alion truth index.

Overview
Company
Impact
Profile match
American Express is a New York financial services company founded in 1850 as an express freight business that became a payments network and card issuer. Unlike the four-party networks it competes with, it issues most of its own cards and operates its own network, which lets it earn merchant discount revenue as well as interest and annual fees, and supports a premium rewards proposition built on travel and lounge access. Its business spans consumer and small business cards, corporate payments, merchant acquiring and travel services, and it is a component of the Dow Jones Industrial Average.

You are a key analytics leader within the Technology Business Enablement (TBE) organization, responsible for transforming complex business challenges into actionable insights that drive product strategy and operational excellence. You will partner closely with Product, Technology, and Business leaders to identify opportunities, frame ambiguous problems, develop analytical approaches, and influence strategic decisions through data.

Success in this role requires more than technical expertise. You must be naturally curious, demonstrate strong business judgment, challenge assumptions with evidence, and communicate complex analytical findings in a way that enables confident executive decision-making.

You will leverage modern analytics, machine learning, generative AI, and business intelligence capabilities while maintaining a strong focus on delivering measurable business outcomes.

Strategic Analytics & Critical Thinking

  • Lead complex and ambiguous business problems from definition through recommendation by developing structured analytical approaches.
  • Frame business questions into measurable hypotheses and identify the data required to validate assumptions.
  • Conduct root cause analysis across product, operational, and portfolio performance challenges, synthesizing multiple data sources into actionable recommendations.
  • Evaluate competing priorities using quantitative analysis, business context, customer impact, and strategic objectives.
  • Challenge assumptions by using data to validate or disprove existing beliefs while encouraging evidence-based decision making.
  • Influence product strategy by providing analytical recommendations that balance customer experience, operational efficiency, technical feasibility, and business value.

Product Analytics

  • Partner with Product Managers, Engineering, and Business stakeholders to define meaningful success metrics, KPIs, OKRs, Portfolio Metrics, Productivity Metrics, Agility Metrics, Capacity, and Demand Metrics.
  • Support roadmap planning through analytical insights that inform prioritization and investment decisions.
  • Participate throughout the Agile delivery lifecycle, contributing to Program Increment planning, backlog refinement, and feature prioritization.
  • Translate customer, operational, and portfolio data into actionable recommendations that improve product outcomes.

Data Science & AI

  • Design, develop, and deploy advanced analytics solutions using Python, Power BI, SQL, APIs, and modern cloud-based analytics platforms.
  • Build and operationalize machine learning models using regression, classification, and predictive analytics techniques.
  • Develop AI-powered automation workflows utilizing approved Generative AI capabilities, LLMs, Retrieval Augmented Generation (RAG), LangChain, vector databases, embeddings, and Agentic AI frameworks.
  • Evaluate AI use cases based on measurable business value, scalability, and operational risk.
  • Contribute to AI governance, model monitoring, and responsible AI implementation.

Leadership & Collaboration

  • Build trusted partnerships across Product, Technology, Architecture, Operations, and Business teams.
  • Influence decisions through compelling storytelling, executive-ready presentations, and evidence-based recommendations.
  • Facilitate discussions that align diverse stakeholder perspectives into shared priorities.
  • Promote a culture where hypotheses are tested, assumptions are challenged, and decisions are grounded in data.

Education & Experience

  • Bachelor's Degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, Business Analytics, Information Systems, or related field required.
  • Master's degree preferred.
  • Four or more years of experience in Data Analytics, Product Analytics, Data Science, Business Intelligence, or related analytical disciplines.
  • Experience working within Agile software delivery environments.
  • Demonstrated success influencing cross-functional teams through data-driven recommendations.
  • Experience managing complex initiatives involving multiple stakeholders and competing priorities.

Required Technical Skills

  • Python
  • SQL
  • Power BI
  • Data Modeling
  • APIs
  • Advanced Excel
  • Machine Learning
  • Statistical Analysis
  • Cloud Analytics Platforms (GCP/LUMI)
  • AI/ML Frameworks
  • Business Intelligence
  • Workflow Automation
  • Data Visualization

Preferred AI Experience

Experience with one or more of the following:

  • Large Language Models (OpenAI, Claude)
  • Retrieval Augmented Generation (RAG)
  • LangChain
  • LangGraph
  • Vector Databases
  • Embeddings (OpenAI, BGE, TF-IDF, GloVe)
  • Agentic AI Frameworks
  • AI Evaluation and Monitoring

Core Competencies

Analytical Excellence

  • Frames ambiguous problems into structured analytical approaches.
  • Develops hypotheses and validates assumptions using quantitative and qualitative evidence.
  • Performs root cause analysis across complex business challenges.
  • Synthesizes multiple data sources into meaningful business insights.
  • Balances analytical rigor with practical business decision making.

Business Judgment

  • Understands how analytics influences product strategy.
  • Recommends priorities based on customer value, business impact, and organizational goals.
  • Recognizes trade-offs and communicates risk effectively.
  • Makes sound recommendations despite incomplete information.

Stakeholder Management

  • Builds trust across cross-functional teams.
  • Influences without formal authority.
  • Communicates with clarity at all organizational levels.
  • Demonstrates ownership and accountability.

Innovation & Learning

  • Demonstrates curiosity and continuous learning.
  • Experiments with emerging AI capabilities responsibly.
  • Encourages innovation while balancing governance and risk.
  • Seeks opportunities to improve processes through automation and analytics.
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