Data Science Manager

Visa Visa · Fintech · Istanbul, Turkey, Turkey

Data Science Manager at Visa to partner with Visa Consulting & Analytics to co-develop data-driven solutions for clients, focusing on acquisition, usage, and retention. The role involves translating business needs into AI solutions, executing data science projects using AI techniques, and identifying market trends to inform product roadmaps. Requires experience with predictive modeling, machine learning, agentic AI systems, LLM integration, and production-grade data manipulation.

What you'd actually do

  1. Develop a deep understanding of Visa’s data assets and value‑added services, and proactively translate them into compelling, commercially viable propositions that address client needs across acquisition, usage, and retention.
  2. Partnering closely with Sales, VCA, and Product teams to identify opportunities, shape client conversations, and convert insights into funded projects.
  3. Collaborate with internal and external stakeholders to define clear business problems, commercial outcomes, and success metrics, translating them into structured analytical and delivery plans.
  4. Execute the Data Science projects, ensuring the use of appropriate statistical and AI techniques to generate clear, business‑centric insights, supported by strong storytelling and impactful visualisation.
  5. Provide subject‑matter expertise and quality assurance across complex Data Science engagements, ensuring analytical rigor, relevance, and alignment with client objectives.

Skills

Required

  • Advanced analytics and AI experience applying a range of techniques (e.g., predictive modeling, machine learning, experimentation, agentic AI systems) to solve real business problems and drive measurable outcomes.
  • Strong hands-on capability in data preparation and feature engineering, including cleaning, transforming, and validating large, complex datasets.
  • Programming skills in Python and SQL, including production-grade data manipulation and ML workflows (e.g., pandas, scikit-learn or equivalent libraries), and the ability to work efficiently with large datasets.
  • Experience designing and implementing agentic AI workflows, including multi-step reasoning pipelines, tool-augmented agents, and orchestration frameworks (e.g., LangChain, LangGraph, AutoGen, or equivalent), with the ability to evaluate agent reliability and manage failure modes in production.
  • Familiarity with large language model (LLM) integration patterns, including prompt engineering, retrieval-augmented generation (RAG), function/tool calling, and memory management within agentic architectures.
  • Proven ability to translate business needs into end-to-end analytical/AI solutions, from problem framing and methodology design to insight delivery and stakeholder adoption — including solutions that leverage autonomous or semi-autonomous AI agents.
  • Ability to communicate complex technical concepts clearly and credibly to non-technical stakeholders, influencing decisions through storytelling, visualization, and structured recommendations.

Nice to have

  • An advanced degree (or equivalent experience) in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field.
  • Experience delivering analytics/AI in payments, banking, consulting, or similarly fast-paced commercial environments.

What the JD emphasized

  • agentic AI systems
  • agentic AI workflows
  • autonomous or semi-autonomous AI agents

Other signals

  • Develop data-driven solutions for clients
  • Translate business needs into end-to-end analytical/AI solutions
  • Leverage autonomous or semi-autonomous AI agents