Advanced Analytics Manager

About the role

Chanel is looking for a hands-on Advanced Analytics & Data Enablement Analyst (titled Advanced Analytics Manager) to turn Korea business data into deeper insight, targeted action and measurable value. Acting as a strategic business partner, the role translates business, media, digital and market questions into analytical solutions and recommendations, combining internal data (client, sales, CRM, campaign, media, digital, social, e-commerce, operational) with external sources such as market trends, consumer sentiment and competitor activity.

Key responsibilities

• Turn business questions into analytical hypotheses, data requirements and success measures, choosing solutions by business value, reliability and interpretability rather than technical complexity.
• Challenge stakeholder assumptions, test multiple hypotheses and communicate uncertainty and data limitations transparently.
• Assess data quality and governance, including missing or duplicate records, inconsistent definitions, lineage and privacy and consent requirements, and build reproducible, documented workflows.
• Explore analytics, AI and automation tools through focused proofs of concept and support the upskilling of business stakeholders.
• Work with the Project Manager and cross-functional teams, clarifying scope, data dependencies, risks and effort estimates, and supporting local testing, UAT and adoption feedback.
• Build, improve or validate advanced analytics and applied machine learning use cases, and produce activation-ready outputs for CRM, campaigns, media, retail, clienteling, digital and e-commerce.

Required profile

• Bachelor's degree or equivalent practical experience in Data Science, Statistics, Mathematics, Computer Science, Engineering, Business Analytics or another quantitative discipline.
• Relevant experience in advanced analytics, data science, CRM/customer analytics, digital analytics, commercial analytics, business intelligence or consulting.
• Ability to communicate analytical methods, findings and limitations clearly to technical and non-technical colleagues.

Required skills

• Strong SQL and Python for data analysis, statistical modelling and machine learning.
• Predictive modelling, classification, customer segmentation, propensity/churn/next-best-action modelling, anomaly detection, recommendation systems and generative AI.
• Understanding of model development concepts such as feature engineering, validation, overfitting, data leakage and interpretability.
• Familiarity with data-governance concepts (quality, lineage, access controls) and preferred: cloud platforms (Azure, AWS, GCP, Databricks, Snowflake), Git, MLOps and data-governance tools.