Junior Applied Scientist (all genders)

Role overview

The ZEOS unit owns Zalando’s partner-facing logistics solutions. Its goal is to bring together fulfillment services into one coordinated offering so that business partners receive a more efficient and profitable logistics experience. Data science, machine learning, and optimization are central to achieving that mission.

This opening is for a junior applied scientist who wants to launch a career by developing practical ML and optimization systems for B2B logistics partners. You will work with an experienced group of applied scientists and machine learning engineers in a cross-functional environment alongside product managers, data engineers, and software engineers.

The team focuses on improving inventory health and order fulfillment efficiency for business partners. With support from senior colleagues, you will contribute to forecasting models for demand, returns, and lead times, stochastic inventory optimization, recommendation systems, and new agentic AI initiatives.

Why this role may be a strong fit

• You will contribute from the beginning rather than spending time on routine tasks, helping shape scientific problems and practical solutions.
• You will work on demanding topics such as stochastic inventory optimization, multi-echelon forecasting, and recommender systems with mentorship from experts.
• You will get exposure to emerging agentic AI work, including how performance is measured and how safety and evaluation guardrails are built.
• You will have the chance to take ideas from research and prototyping through to production deployment and impact tracking.
• Your work will support platform-level services used by many partners, contributing to more efficient and sustainable e-commerce logistics at scale.

What we are looking for

• A background in a quantitative discipline; a master’s degree or higher is preferred.
• Up to 2 years of practical experience, which may include internships, working student roles, or substantial thesis or academic project work.
• Strong grounding and hands-on exposure in at least one of these areas: machine learning or deep learning for time-series forecasting, operations research and optimization, machine learning engineering fundamentals, or agentic AI and MCP evaluation frameworks.
• Comfort working with SQL and Python, plus some experience handling datasets.
• The ability to explain scientific ideas clearly and communicate well with business stakeholders.
• A collaborative, learning-focused mindset and willingness to ask questions and grow through hands-on work.
• A fit with the team’s “High Challenge, High Support” culture and openness to direct feedback.

Skills and tools

Relevant work for this role includes forecasting methods such as LGBM, ARIMA, Prophet, and transformers; optimization approaches such as stochastic inventory models, linear and integer programming, and Monte Carlo simulation; and practical engineering basics such as git, batch processing, and basic software testing.

Inclusive hiring

Zalando states that it hires based on qualifications, merit, and business needs. Applications are welcomed from candidates of all gender identities, sexual orientations, personal expressions, racial identities, ethnicities, religious beliefs, and disability statuses. Applicants are asked not to include a photo, age, or marital status in the CV. Candidates may also request accommodations during the hiring process if needed.

Benefits and offer

• Employee share participation program.
• 40% discount on fashion and beauty products sold and shipped by Zalando, plus 30% off Lounge by Zalando and partner discounts.
• Two paid volunteering days each year.
• 27 vacation days per year at the start for full-time employees.
• Family support services, including counseling.
• Health and wellbeing benefits, including Wellhub (formerly Gympass).
• Mental health support and coaching.
• Access to development through a learning platform and biannual peer reviews.

Additional information

The employer mentions that benefits may vary and that candidates can ask the Talent Acquisition Partner for more details. No salary, stipend, or number of vacancies is provided in the source.