Applied Scientist - ML - Retail Data Products

18 Aug 2021
20 Nov 2021
Have you ever experienced the frustration that the size of the jeans you want to buy is out of stock? Do you want to know how we at Zalando tackle this problem? Come join us! We are a team of applied scientists and engineers aiming to provide a flawless shopping experience by always making available the right sizes for our customers, and to achieve unit economics by smart stock management. As the Applied Scientist in the Retail Data Products Team, you'll be working with other Data Scientists and stakeholders to build the ML solutions that help Zalando place the right orders for the right articles at the right time.

  • Own the whole development cycle end to end, from exploring gaps in model performance to model production. Reduce abstract business requirements into manageable scientific problems
  • Proficient in analyzing data and building models with common tools like Python, R, Scala, SQL. Leveraging commonly used ML/optimization packages (e.g. sklearn, scipy) as well as deep learning frameworks to solve critical business problems. Derive insights and help steer the development of a product
  • Communicate verbally and in writing to business customers with various levels of technical knowledge, educating them about our systems, and enabling them to interact with our data products. Present scientific insights in plain words
  • Drive fast and iterative improvements to established models. Actively reduce uncertainty by forming/validating hypotheses in problem solving
  • Fostering an inclusive and diverse team environment

  • Master degree or above (PhD) in quantitative economics, statistics, mathematics, physics, computer science, machine learning or a related quantitative discipline
  • At least 3 years of industry experience (1 years with PhD degree) in building end-to-end machine learning products, using both statistical methods and machine/deep learning techniques. Strong mathematical background and analytical skills. Able to reduce research uncertainty and quickly converge to feasible solutions
  • Experience working with engineers to convert prototypes into production models. Industry experience in building deep learning solutions with PyTorch and Tensorflow. Experience using distributed computing frameworks such as Spark to leverage large datasets in order to drive business outcomes. Solid software development skill set (e.g. test driven development, strong documentation). Experience with AWS Sagemaker is a plus
  • Excellent verbal and written communication skills with a curious and self-starter mindset
  • Experience in Promoting inclusive cultural practices

  • 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, PME family service and parent & child rooms* (*available in select locations)

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.
Zalando is Europe’s leading online platform for fashion, connecting customers, brands and partners across 17 markets. We drive digital solutions for fashion, logistics, advertising and research, bringing head-to-toe fashion to more than 34 million active customers through diverse skill-sets, interests and languages our teams choose to use.

Please note that all applications must be completed using the online form - we do not accept applications via email.

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