Job Description

This is a people + strategy + technical leadership role focused on applying data science across global business functions such as:

• Retail performance
• Customer analytics
• Supply chain optimization
• Marketing analytics
• E-commerce insights
• Merchandising & demand forecasting

You would act as the bridge between business leadership and technical teams.

🎯 Key Responsibilities (What You’ll Likely Do)

1️⃣ Team Leadership

• Lead cross-functional data scientists and analysts
• Mentor and guide technical execution
• Manage project priorities and roadmap

2️⃣ Strategy & Decision Support

• Translate business problems into data solutions
• Provide insights to senior stakeholders
• Drive data-driven decision making globally

3️⃣ Advanced Analytics & ML

• Develop predictive models
• Apply machine learning and data mining
• Optimize business performance metrics

4️⃣ Deployment & Scale

• Work with engineering teams
• Deploy analytics solutions into production
• Use big data technologies (Spark, Hadoop)

🧠 Required Skills

Technical

• Python
• SQL
• Machine Learning
• Data Mining
• Big Data (Spark / Hadoop)
• Analytics & BI

Leadership

• Stakeholder management
• Communication with executives
• Project ownership
• Strategic thinking

🎓 Experience Level

Minimum requirements:

• 5+ years experience

But realistically this role fits:

6–10 years experience (Manager level)

This is not entry-level and not purely technical — it’s leadership focused.

💼 Industry Context — Fashion / Retail Data Science

Retail data science focuses heavily on:

• Customer segmentation
• Pricing optimization
• Demand forecasting
• Inventory planning
• Personalization
• Marketing attribution

Compared to tech companies, retail often provides:

✅ Strong business impact
✅ Broader responsibilities
⚠️ Slightly lower technical complexity

💰 Salary Insight (Typical U.S.)

Estimated compensation:

• Base: $130k – $180k
• Bonus: 10–20%
• Senior markets: $190k+

Luxury brands sometimes offer strong perks and brand prestige.

⭐ Pros of This Role

✅ Global exposure
✅ Leadership experience
✅ Business strategy involvement
✅ Good step toward Director level
✅ Cross-functional influence

⚠️ Possible Cons

⚠️ Less cutting-edge AI compared to big tech
⚠️ Stakeholder-heavy role (meetings)
⚠️ Retail margins pressure → fast decisions

📈 Career Growth Path

From here you could move to:

• Director of Data Science
• Head of Analytics
• VP Data / AI
• Chief Data Officer

Luxury retail leadership experience is valuable.

🆚 Compared to Other Roles You Shared

Role	Level
PALACE Data Scientist	Entry
State Farm Data Scientist	Mid
Expedia Data Scientist II	Mid
Walmart Senior DS	Senior
Ralph Lauren Global Manager	Manager
Sam’s Club Group Director	Executive

✅ Who This Role Is Best For

Someone who:

• Has strong analytics + ML foundation
• Wants leadership responsibilities
• Enjoys business strategy
• Wants global exposure
• Has 5–10 years experience