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
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-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
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-Build and continuously enhance demand forecasting solutions at product, category, and channel level—integrating historical trends, business input, and statistical modeling.
-D
esign 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
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-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
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-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
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-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
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-Strong analytical problem-solving skills
, with a
bility to break down complex questions into structured analysis, with clear and concise communication.
-B
usiness acumen in an eCom or retail environment
, ideally s
trong 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
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-M
aster 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
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-Plus would be considered:
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-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
-E
xperience supporting merchandising, planning, or omnichannel strategy.
-Vendor
management
and project management
skills.
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-Fluent English both verbally and written
