Applied Scientist (All Genders)

Sei unter den ersten Bewerbenden.
Nur für registrierte Mitglieder
Berlin
EUR 70.000 - 100.000
Sei unter den ersten Bewerbenden.
Vor 4 Tagen
Jobbeschreibung

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Applied Scientist (All Genders)

Apply locations Berlin time type Full time posted on Posted 3 Days Ago job requisition id 2719994

The Fashion Recommendations Team is at the core of Zalando’s browsing experience and fashion discoverability. Our purpose is to play a key role in making Zalando the starting point of fashion by providing recommendations that meet our customers’ fashion needs and wishes while understanding their unique style, preferences and interests. As part of the larger Search and Recommendations department, you will work with colleagues in Search to optimize the customer journey to drive commercial impact by developing new recommendations and optimizing current ones.

As an Applied Scientist , you will: design ML/AI solutions that serve our customers’ needs; build data pipelines and models using, for example, AWS, Python and TensorFlow; define hypotheses and A/B test them to validate your use case; and deploy solutions into production to serve more than 52 millions of customers. You will focus on new state-of the art solutions, such as LLM-based recommendations, to elevate the impact of Recommendation use cases and devise new opportunities to guide the customer down the purchase funnel.

You will be part of a talented cross-functional team made up of Applied Scientists, Data Engineers and Backend Engineers. You will also count with the support of Product Managers and Designers to create the customer experience that you will deliver.

WHERE YOUR EXPERTISE IS NEEDED

  • Apply your expertise to solve real-life ML use cases that impact millions of customers

  • Design and implement models and data pipelines that scale up to serve thousands of requests per second during Black Friday

  • Contribute to the team’s roadmap by creating an incremental delivery plan for our use cases

  • Work together with product managers, designers, and engineers to deliver impactful solutions that change the way our customers buy fashion

  • Keep up to date with the state of the art in recommender systems and contribute to the data science community both internally in Zalando and externally in meetups and conferences

WHAT WE’RE LOOKING FOR

  • Ph.D. or, M.Sc. and 4 years of working experience

  • Great coding skills in Python, Java and / or Scala, and hands on experience with TensorFlow and cloud computing frameworks

  • Deep understanding of theory behind the state of the art methods in your domain (deep learning, LLM-based recommendations) as well as a broad overview of machine learning methods and best practices.

PERKS AT WORK

  • Culture of trust, empowerment and constructive feedback, open source commitment, meetups, game nights, 70+ internal technical and fun guilds, knowledge sharing through tech talks, internal tech academy and blogs , product demos, parties & events

  • Competitive salary, employee share shop, 40% Zalando shopping discount, discounts from external partners, centrally located offices, public transport discounts, municipality services, great IT equipment, flexible working times, additional holidays and volunteering time off, free beverages and fruits, diverse sports and health offerings

  • Extensive onboarding, mentoring and personal development opportunities and an international team of experts

  • Relocation assistance for internationals

We celebrate diversity and are committed to building teams that represent a variety of backgrounds, perspectives and skills. All employment is decided on the basis of qualifications, merit and business need.

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It’s the perfect time to join Zalando on our journey, from being a pioneer in the world of e-commerce, to the Starting Point for Fashion in Europe. We connect customers, brands, and partners across 25 markets.

Help us drive digital and sustainable solutions for fashion, logistics, advertising and research, bringing head-to-toe fashion to 50 million active customers through a team of diverse skill-sets, cultural backgrounds, and interests.