обновлено 7 дней назад
Data Product Manager, Dynamic Assortment - (Quick Commerce)
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Data Product Manager, Dynamic Assortment - (Quick Commerce) (Machine Learning/Data Products): Building adaptive, personalized digital shelf products for quick commerce with an accent on machine learning models, product curation, and customer discovery. Focus on designing A/B tests, developing recommendation and automated merchandising models, and monitoring conversion, average order value, and retention metrics.
Location: Berlin, Germany; hybrid model with attendance at the Berlin campus 2 days a week
Company
is a global local delivery and quick commerce platform operating in around 65 countries and headquartered in Berlin.
What you will do
- Define and prioritize the product vision, roadmap, and initiatives for dynamic assortment data products.
- Partner with data science and engineering teams to develop and deploy machine learning models for product curation, personalization, and substitution.
- Design, run, and analyze A/B tests to improve algorithm performance and business KPIs.
- Lead initiatives involving customer-facing category trees, category recommendations, automated merchandising, and cross-listing.
- Align stakeholders across merchandising, catalog, data analytics, platform product, and commercial teams.
- Establish and monitor metrics covering model performance, customer satisfaction, conversion, average order value, and retention.
Requirements
- 3–5 years of product management experience focused on technical, data-centric products in e-commerce, marketplaces, or quick commerce.
- Experience building products that use large datasets, machine learning models, data pipelines, and rigorous experimentation.
- Strong understanding of machine learning concepts, data modeling, and experimentation frameworks.
- Proficiency in SQL and data notebooks, with the ability to derive insights and define success metrics.
- Experience leading cross-functional teams and communicating complex technical concepts to non-technical stakeholders.
- Ability to translate customer needs and business opportunities into data-driven product features.
Nice to have
- Experience with large-scale personalization or recommendation systems.
- Familiarity with the lifecycle of machine learning models.
- Bachelor’s degree in Data Science, Computer Science, or a related technical field.
- Experience with merchandising or retail analytics.
- Experience breaking down complex initiatives, building consensus, and driving execution.
Culture & Benefits
- Hybrid working model with regular face-to-face collaboration.
- 27 days of holiday, plus an additional day in the second and third years of service.
- €1,000 educational budget, language courses, parental support, and Udemy Business access.
- Health checkups, meditation, gym access, and other wellbeing benefits.
- Employee share purchase plan, sabbatical bank, public transportation discount, insurance, and corporate pension plan.
- Digital meal vouchers, food vouchers, and corporate discounts.
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