Senior Manager - Data Science

Responsibilities

• Lead Data Science Projects: Design, develop, and deploy predictive and explanatory analytical solutions that address critical business problems using machine learning, NLP, and generative AI.
• Drive Analytics: Deliver high-impact analytics to inform strategy by developing actionable insights into Sales and client behavior. Introduce new approaches to transform complex behavioral data and influence decision-making across the organization.
• GenAI Analytics Use Case Development: Lead key workstreams in the design, development, and operationalization of a GenAI-enabled analytics solution that synthesizes internal performance and external competitive signals into actionable insights, with defined success metrics and ongoing monitoring.
• Develop Modeling Capabilities: Build and evaluate models using modern ML frameworks (e.g., TensorFlow, PyTorch), focusing on scalability, performance, and interpretability.
• People Leadership: Lead a team of high-performing data scientists.
• Collaborate Across Teams: Establish and maintain close relationships with key cross-functional stakeholders to understand business strategies, develop goals, and address opportunities.
• Develop Scalable Solutions: Architect and deploy robust, efficient, and scalable data pipelines and modeling solutions using modern cloud and distributed compute patterns.
• Lead Innovation Through External Perspective: Stay current on advancements in machine learning, deep learning, and generative AI; evaluate emerging approaches; translate theoretical advances into practical, scalable solutions that advance business outcomes. Challenge the status quo and demonstrate strong curiosity.
• Define Performance Indicators: Lead analytics and measurement across key performance indicators. Own stakeholder and executive-level communications on initiative progress, including automated monthly measurement tied to specific strategic initiatives.
• Communicate Insights Effectively: Present findings, recommendations, and results to both technical and non-technical audiences, including executive leadership, through clear reports, visualizations, and presentations, to enable data-driven decision-making.

  Qualifications

  • 4-5 years of relevant work experience.
  • Bachelor’s degree required, preferably in a quantitative field (e.g., Economics, Finance, Computer Science, Mathematics/ Statistics, Engineering).
  • Strong analytical and conceptual thinking acumen, with ability to translate complex, unstructured business problems into quantitative models.
  • Leverage external insights and tools (from academia or other industries) where needed.
  • Able to articulate key findings to senior leadership and stakeholders, leveraging insights to influence business decisions.
  • Familiarity with machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and experience applying algorithms to real-world business problems.
  • High proficiency in Python/SQL is required; experience with Hadoop and Spark is a plus.
  • Experience with data querying and distributed analytics tools (e.g., Hive, PySpark, BigQuery) is required.
  • Experience in a Big Data environment, including data mining and data processing.
  • Ability to address performance issues and to manipulate both structured and unstructured data.
  • Demonstrable experience with data visualization and reporting tools (e.g., matplotlib, seaborn, Tableau).
  • Proficiency with industry-recognized ETL methods, processes and standards.
  • Advanced knowledge of Microsoft Office Suite (Excel pivot tables, deck-writing).
  • Ability to work independently as well as collaboratively in a dynamic, cross-functional environment, with a strong attention to detail and passion for learning.

    Preferred Qualifications

    • Masters/PhD in a quantitative field (Computer Science, Statistics, Econometrics, Mathematics, Physics, Operation Research, Engineering, etc.).
    • 2+ years’ experience of applying machine learning techniques to real-world business problems, including exposure to production ML and/or GenAI (e.g., LLM prompting, RAG, evaluation).
    • Stakeholder management at the executive level.
    • People leadership experience.

      About Us

      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.