PURPOSE & OVERALL RELEVANCE FOR THE ORGANIZATION

As the eCommerce Performance & Advanced Analytics Manager, you are responsible for delivering end-to-end analytical services and insights that drive performance across the full digital commerce landscape. This includes owning performance reporting, demand forecasting, and advanced analytics that uncover business opportunities and drive fact-based decision making.

You will work across all eCom channels (own site, app, partner platforms), partnering with business stakeholders to translate data into actionable strategies. You will also lead the development of machine learning models and advanced analytics pipelines that support scalable analytics and automation.

 

KEY RESPONSIBILITIES

eCommerce Performance & Forecasting Analytics

Lead the full-cycle eCommerce performance reporting across multiple levels (article, category, channel), combining backend data pipelines with frontend dashboards. Develop structured analytics frameworks to measure and explain business performance, answer key questions, and guide decision-making. Translate analytical models into business-friendly tools and interfaces (e.g., PowerBI, self-service notebooks, AI agents, etc). Build, maintain and improve various business performance tracking and analytics dashboards, both frontend and backend Partner with eCom business teams to identify performance drivers and develop strategic actions based on insights.

 

Advanced Analytics & Forecasting

Build and continuously enhance demand forecasting solutions at product, category, and channel level—integrating historical trends, business input, and statistical modeling. Design and implement advanced analytics use cases (e.g. markdown optimization, product similarity) to optimize business outcomes. Explore machine learning and AI applications to enable proactive performance management and self-service analytics tools.

 

Data Engineering & Solution Management

Collaborate with local/global data and analytics teams to align on data models, pipelines, and tooling for scalable analytics delivery. Oversee the work of data engineering vendors to ensure reliable, efficient data preparation pipelines. Own the delivery of analytics-ready eCom performance related data (sellout, inventory, etc.).

 

KEY RELATIONSHIPS

KOREA e-com team KOREA Function teams (Finance, Data & Analytics) Global analytics teams (Digital analytics, Data & Analytics) External vendors

REQUISITE EDUCATION AND EXPERIENCE / MINIMUM QUALIFICATIONS

Degree with focus in Statistics, Economics, Math, Data Science, Business or related field preferred, or equivalent combination of education and experience 4+ years of experience working in Digital Analytics, Data Analytics, Business Intelligence, Demand forecasting & planning, Data Science or equivalent experience Experience in demand forecasting, time-series modeling, and/or commercial performance analytics and reporting. Experience in e-commerce environment Strong hands-on experience in data processing, analytics, and machine learning using SQL, Python, PowerBI.

 

KNOWLEDGE & SOFT SKILLS

Strong analytical problem-solving skills, with ability to break down complex questions into structured analysis, with clear and concise communication. Business acumen in an eCom or retail environment, ideally strong understanding and proven experience in digital and e-commerce analytics, with deep knowledge of multi-platform e-commerce ecosystems, KPIs, and performance optimization. Ability to work in cross-functional, multicultural teams and manage external vendors. Experience presenting solutions or findings to both technical and non-technical stakeholders. Self-driven, detail-oriented, and continuously curious about emerging analytics and AI solutions.

 

HARD SKILLS

Master of analytics tools including SQL, Python, PowerBI Hands-on experience with big data platforms such as databricks, AWS or Azure and coding tools such as Jupyter Notebook, Git, etc. Familiar with sales KPIs (gross sales, net sales, margin, etc.) Experience in forecasting and predictive models Experience in data engineering, esp. data modeling and pipeline building

 

Plus would be considered: Experience in building data science or machine learning models Experience deploying ML models into operational pipelines or dashboards. Experience in financial planning and analytics, farmiliar with financial KPIs Experience supporting merchandising, planning, or omnichannel strategy. Vendor management and project management skills.

 

Fluent English both verbally and written