Technical Lead

About the role

The Global Data Science group supports bank and merchant partners by leveraging extraordinarily rich datasets that span more than 3 billion cards globally and capture more than 100 billion transactions annually. The team's focus is on building innovative solutions that deliver immediate business impact for highly analytical partners.

Responsibilities

• Use and build predictive models to innovate and optimize customer experiences, revenue generation, data insights, advertising targeting, and other business outcomes.
• Provide technical leadership within a team that generates business insights from large-scale data, identifies impactful recommendations, and communicates findings to clients.
• Leverage AI coding tools (e.g., GitHub Copilot, Claude Code, Cline, OpenAI Codex) to accelerate development.
• Apply data science techniques and innovative thinking to solve complex business problems and help clients achieve their strategic objectives.
• Lead projects from scoping through delivery while engaging with internal and external stakeholders.
• Develop and deploy machine learning pipelines, including Retrieval-Augmented Generation (RAG) components, to deliver actionable insights from transaction data.
• Build agentic AI systems with multi-step reasoning, tool utilization, and memory capabilities for complex decision-making workflows.
• Implement Generative AI evaluation methodologies covering quality, bias, safety, and compliance.
• Engage directly with clients to understand business challenges and influence decision-making through data-driven insights.
• Identify opportunities to transform analytical solutions into scalable products that can support multiple clients.
• Collaborate with partners across the organization to identify opportunities for leveraging data to drive business solutions.
• Synthesize ideas and proposals into clear written recommendations and participate in productive discussions with internal and external stakeholders.
• Provide mentorship in modern analytics techniques, business applications, Generative AI best practices, and experimentation frameworks.
• Prioritize and lead multiple data science initiatives involving diverse cross-functional partners.

Qualifications

• Basic Qualifications: 4 years of work experience with a Bachelor's degree, or at least 2 years of work experience with an Advanced Degree (e.g., Master's, MBA, JD, MD), or 0 years of work experience with a PhD.
• Preferred Qualifications: 6–8 years of work experience with a Bachelor's degree, or 4–6 years of work experience with an Advanced Degree (e.g., Master's, MBA, JD, MD), or 3 years of work experience with a PhD.
• 4+ years of experience in data-driven decision-making or quantitative analysis, including exposure to LLMs and Generative AI applications.
• Master's degree in Statistics, Operations Research, Applied Mathematics, Economics, Data Science, Business Analytics, Computer Science, or a related technical field.
• Experience analyzing large datasets using programming languages and technologies such as Python, SQL, Hive, and/or Spark.
• Experience building predictive and descriptive statistical models using machine learning toolkits, Jupyter Notebooks, Python, or similar platforms.
• Experience with LLM orchestration frameworks (e.g., LangChain or similar), vector databases, and embedding models.
• Strong ability to communicate evidence-based insights and deliver clear, actionable recommendations to both technical and non-technical audiences.
• Previous experience in financial services, payments, credit cards, banking, or merchant analytics is preferred, but not required.