Client Consulting Manager

Visa Visa · Fintech · Singapore

This role focuses on delivering data-driven insights and solutions for clients, leveraging advanced analytics and machine learning. The Senior Client Consulting Manager will lead end-to-end analytics engagements, including problem framing, model development, deployment, monitoring, and business adoption. The role involves translating complex data into actionable strategies, supporting the development of production-ready AI/ML solutions, and managing multiple analytics projects. Key responsibilities include conducting deep-dive analysis, building and validating models, ensuring analytical rigor, and translating outputs for non-technical stakeholders. Experience with Python, data platforms, and ML lifecycle concepts is required, with a focus on deploying AI/ML models in real-world business contexts.

What you'd actually do

  1. Conduct deep-dive analysis on large-scale transactions and customer data to generate actionable business insights
  2. Build, validate, and interpret statistical and machine learning models (e.g., propensity, segmentation, forecasting, uplift)
  3. Execute the end-to-end analytics workflow: problem framing, data exploration, feature engineering, modeling, and validation
  4. Ensure analytical rigor, data quality checks, and QA on all deliverables
  5. Translate analytical outputs into clear insights and recommendations for non-technical stakeholders

Skills

Required

  • Python
  • data platforms (Hadoop, Hive, Impala, or cloud-based equivalents)
  • statistical and ML techniques (regression, classification, clustering, tree-based models, etc.)
  • analytics projects end-to-end in a fast-paced, client-facing environment
  • deploying AI/ML models in real‑world business contexts

Nice to have

  • model deployment and ML lifecycle concepts

What the JD emphasized

  • Experience supporting/owning deployment of AI/ML models in real‑world business contexts (production or pilot environments)

Other signals

  • Leverage AI/ML techniques where appropriate to enhance scalability, automation, or personalization use cases
  • Support the deployment of data science models into production environments in partnership with engineering and platform teams
  • Contribute to model monitoring, performance tracking, and periodic recalibration
  • Experience supporting/owning deployment of AI/ML models in real‑world business contexts (production or pilot environments)