Senior/ Principal Applied Scientist - Assortmen...
@ Zalando

Berlin, Germany
On Site
Full Time
Posted 4 days ago

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XXXXXXXX XXXXXXXXXXXXX XXXXXX***** @zalando.com
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Job Details

About the Role & Team

Zalando is the Starting Point for Fashion in Europe. This greenfield opportunity is for a Senior Applied Scientist to architect a scientific foundation that transforms assortment optimization from manual curation to an intelligent, automated process.

Responsibilities

  • Architect the end-to-end scientific strategy for assortment optimization.
  • Develop sophisticated forecasting models using time-series and deep learning.
  • Solve large-scale optimization challenges using operations research techniques.
  • Pioneer sequential decision-making through model-based online optimization or reinforcement learning.
  • Design experiments and build causal models to measure impact.
  • Lead and mentor a team of scientists and engineers.

Requirements

  • PhD or Master's in Computer Science, Operations Research, Machine Learning, Statistics, or Economics.
  • 8+ years of industry experience solving large-scale problems.
  • Expertise in machine learning, operations research, and causal inference.
  • Strong programming skills in Python and experience with big data technologies.
  • Exceptional communication skills with an entrepreneurial, strategic mindset.

Our Offer

Benefits include employee shares, generous product discounts, paid volunteering, a hybrid working model, work-from-abroad options, relocation assistance, extensive vacation days, and robust family and wellbeing support. Learn more from your Talent Acquisition Partner.

Key skills/competency

  • Assortment Optimization
  • Forecasting
  • Machine Learning
  • Operations Research
  • Reinforcement Learning
  • Causal Inference
  • Python
  • Big Data
  • Mentorship
  • Scientific Strategy

How to Get Hired at Zalando

🎯 Tips for Getting Hired

  • Research Zalando's culture: Study their mission and core products thoroughly.
  • Customize your resume: Highlight optimization and ML project experience.
  • Prepare examples: Detail previous large-scale problem solutions.
  • Practice technical interviews: Focus on ML, operations research and causal inference.

📝 Interview Preparation Advice

Technical Preparation

Review forecasting and deep learning libraries.
Practice operations research problem solving.
Test reinforcement learning model implementations.
Brush up on Python and big data processing.

Behavioral Questions

Discuss past leadership experiences briefly.
Explain decision-making in ambiguous projects.
Describe mentoring and team collaboration examples.
Share conflict resolution in technical teams.

Frequently Asked Questions