Position: Advanced Analytics Manager

About the Role:
 
We are looking for a hands-on Advanced Analytics & Data Enablement Analyst to help turn Korea business data into deeper insight, targeted action, and measurable value.
Acting as a strategic business partner, the role will translate business, media, digital, and market questions into tailored analytical solutions, actionable recommendations, and measurable outcomes. The successful candidate will take an expansive view of available data, combining internal sources—including client, sales, CRM, campaign, media, digital-platform, social, e-commerce, and operational data—with relevant external sources such as market trends, consumer sentiment, competitor activity, seasonality, and macroeconomic indicators.
The ideal candidate is naturally curious, analytically rigorous, and commercially minded. They do not stop at the first answer—they investigate root causes, challenge assumptions, test hypotheses, validate findings through appropriate analysis and stakeholder feedback, and consider alternative approaches when new evidence suggests a better path.
The role will help Korea move from reporting what happened to understanding why it happened, what is likely to happen, what action should be taken, and how impact should be measured.

Key Responsibilities:

• Translate business questions into structured analytical hypotheses, data requirements, analytical approaches, intended business actions, and success measures.
• Select solutions based on business value, reliability, interpretability, scalability, feasibility, and adoption potential—not technical complexity alone.
• Apply appropriate model-validation methods, baseline comparisons, and performance metrics.
• Document analytical methods, assumptions, limitations, outputs, and recommendations clearly.
• Challenge stakeholder assumptions and validate whether observed patterns are genuine insights, data artefacts, process changes, or temporary effects.
• Develop and test multiple hypotheses and solution options where appropriate.
• Adapt analytical approaches when data, findings, or business requirements indicate a more effective solution.
• Communicate uncertainty, data limitations, risks, and the strength of evidence transparently.
• Assess whether available data is fit for purpose before using it for reporting, analytics, activation, machine learning, or AI use cases.
• Identify data-quality issues such as missing or duplicate records, inconsistent definitions, unreliable joins, incomplete histories, late-arriving data, source-system changes, and potential bias.
• Support fit-for-purpose data governance for analytics use cases, including business definitions, data ownership, source documentation, lineage awareness, data-quality checks, and appropriate data use.
• Understand and apply relevant privacy, consent, access-control, and responsible-data-use requirements.
• Build reproducible, well-organized, and documented analytical workflows.
• Escalate early when data is not sufficiently reliable or appropriate for a business decision, activation, dashboard, model, or AI use case.
• Stay informed about relevant analytics, AI, automation, data-visualization, and data-platform technologies.
• Conduct focused proofs of concept to evaluate emerging tools and methods based on value, practicality, governance, cost, risk, and adoption potential.
• Contribute to a culture of experimentation, knowledge sharing, continuous learning, analytics best practices, and evidence-based decision-making.
• Support the upskilling of business stakeholders by helping teams understand, interpret, and act on analytical insights effectively.
• Work closely with the Project Manager, providing technical input into project scope, data dependencies, risks, effort estimates, solution options, and delivery plans.
• Participate in stakeholder discussions to clarify questions, validate assumptions, and explain analytical findings in clear and practical language.
• Partner effectively with local and regional data, CRM, digital, media, tech, retail, commercial, and business teams.
• Leverage relevant advanced models and regional capabilities to support local business operations and decision-making.
• Assess regional data, analytics, CRM, and AI capabilities for Korea business relevance, data readiness, local process fit, and value potential.
• Support local testing, user acceptance testing, adoption feedback, and enhancement requests for relevant regional capabilities.
• Demonstrate a hands-on ownership mindset: proactively investigate issues, propose solutions, and help remove barriers to delivery.
• Know when to involve subject-matter experts or escalate structural data, technology, privacy, security, or process risks.
• Build, improve, or validate priority advanced analytics or applied ML use cases with defined business value.
• Produce activation-ready outputs that can support CRM, campaign, media, retail, clienteling, digital, e-commerce, or commercial actions.
• Establish disciplined practices for data validation, KPI documentation, analytical documentation, and reproducible work.
• Identify critical data-definition, data-quality, consent, access, or data-structure issues early and mobilize the appropriate owners to address them.
• Help strengthen Korea’s capability to move from reporting what happened to understanding why it happened, what is likely to happen, what action to take, and how to measure the outcome.

Scope Clarification:
This role is expected to actively contribute to data governance, quality, KPI standards, , analytical reliability, and responsible AI practices for analytics and activation use cases. It is not expected to independently own enterprise-wide data-governance policy, resolve all source-data issues, build all technology integrations, or replace specialist data engineering, privacy, security, media, or IT functions.
The role’s responsibility is to ensure that analytical work is trustworthy, actionable, measurable, and responsibly delivered—and to bring the appropriate teams together when structural issues need resolution.
 
Academic / Professional Qualifications:

• Bachelor’s degree or equivalent practical experience in Data Science, Statistics, Mathematics, Computer Science, Engineering, Business Analytics, or another quantitative discipline.

Required Technical Capabilities & Experience:

• Relevant experience in advanced analytics, data science, CRM/customer analytics, digital analytics, commercial analytics, business intelligence, consulting, or a similar data-focused role.
• Strong SQL skills and confidence working with complex, large, and imperfect data sets from multiple sources.
• Proficiency in Python for data analysis, statistical modeling, and machine learning.
• Practical experience in one or more of the following:
  • Predictive modeling or classification
  • Customer segmentation or clustering
  • Propensity, churn, customer-value, or next-best-action modeling
  • Anomaly detection
  • Recommendation or personalization
  • generative AI, or analytics automation
• Understanding of key model-development concepts, including feature engineering, train/test validation, baseline comparison, performance metrics, overfitting, data leakage, interpretability, and bias.
• Demonstrated ability to conduct deep analysis, formulate hypotheses, validate findings, and develop evidence-based recommendations.
• Familiarity with core data-governance concepts, including data quality, business definitions, data ownership, data lineage, access controls, and responsible data use.
• Ability to communicate analytical methods, findings, recommendations, and limitations clearly to technical and non-technical colleagues.

Preferred Qualifications

• Experience activating analytical outputs in CRM, marketing, clienteling, retail, digital, commercial, or planning processes.
• Experience operationalizing analytical solutions into recurring workflows, customer journeys, or decision tools.
• Experience with cloud data or analytics environments such as Azure, AWS, GCP, Databricks, Snowflake, or equivalent.
• Familiarity with Git, version control, reusable code, code review, or reproducible analytical workflows.
• Exposure to MLOps, model monitoring, data pipelines, APIs, or deployment practices.
• Familiarity with data catalog, data-quality, or data-governance tools.
• Understanding of privacy, consent, and responsible-AI considerations when working with customer data.Evidence of self-directed learning, proof-of-concept development, automation, or adoption of new analytical technologies.