Applied Scientist (all genders) - B2B Data Science and Algorithms

Applied Scientist (all genders) - B2B Data Science and AlgorithmsThe ZEOS department is responsible for all partner-facing Zalando Logistics Solutions. We provide a holistic approach to delivering the fulfillment solutions that meet our partner’s needs by unifying these services under a single umbrella. We aim to provide our partners with a profitable fulfilment experience, and we see that to do this, Machine Learning, Operations Research and Data-driven solutions will play a pivotal role. We are seeking an Applied Scientist who is fueled by the desire to build innovative and impactful ML/Optimization systems for our B2B logistics partners. You will join an existing team of Applied Scientists, and Machine Learning Engineers and work in a cross-functional setup with Product Managers, Data and Software Engineers. The team’s focus is helping our B2B partners improve inventory health and order fulfilment efficiency. You will be building various ML/DL forecasting models (demand, returns, lead-times), stochastic inventory optimization solutions, recommendation services and emerging Agentic AI systems that generate these key insights for our partners and enable them to make data-driven decisions about their article assortment and inventory. You will play a key role in a cross-functional team!WHY YOU SHOULD BE INTERESTED…Shape the Product from Day One: You won't be a "model factory" stuck in a silo! You will be a core scientific partner, working directly with product and user research to define the problem, not just solve it. Your expertise will directly influence the product roadmap and identify new opportunities to create value for our partners.Solve High-Stakes, Complex Scientific Problems: This is your chance to go beyond standard forecasting. You'll be tackling cutting-edge challenges in stochastic inventory optimization, multi-echelon demand forecasting (including returns and lead-times), and building recommender systems for assortment planning. Your work will be at the absolute core of our partners' profitability.Pioneer Agentic AI & Robust Evaluations: As we explore opening up MCP servers to our partners, you will contribute to defining how we measure their performance/success in the user’s agentic journey. You will design and implement rigorous Agentic AI evaluation frameworks/metrics (e.g., LLM-as-a-judge, trajectory evaluation, and safety guardrails) to ensure the autonomous systems connecting to the MCPs act reliably and logically on behalf of our B2B partners.End-to-End Ownership: You will have the autonomy to see your ideas through from initial research and prototyping all the way to production. You will define the metrics for success, collaborate with engineers to deploy your models as scalable services, and monitor their real-world impact on partner KPIs.Drive Impact at a multi-merchant scale: Your solutions won't just help one partner. You will be building platform-level services that scale across hundreds of diverse partners/merchants, directly influencing millions of euros in merchandise value and shaping the future of a more sustainable and efficient e-commerce logistics network. WE’D LOVE TO MEET YOU IF…Educational background in a quantitative field - Masters degree or higher preferred.3+ years of hands-on industry experience in an Applied Scientist, Data Scientist, or Research Scientist role, applying scientific methods to solve business problems.Industry demonstrated knowledge and skills in at least one the following areas:Machine Learning or Deep Learning, particularly applied for time-series forecasting (e.g., LGBM, ARIMA, Prophet, Transformers, Nixtla, Darts,...)Machine Learning Engineering (e.g. service design, batch processing, GPU computing, git, docker, CI/CD, software testing)Operations Research and Optimization, (e.g., stochastic inventory models, linear/integer programming, Monte Carlo simulations, …)Agentic AI & MCP evaluation frameworksProficiency in SQL and experience working with large-scale datasets.Strong communication skills with the ability to explain complex scientific concepts to product managers and business stakeholders. A collaborative, product-oriented mindset and a desire to see your work create real-world impact.An entrepreneurial mindset, you value a diverse and fast-paced business environment, and hence balance your passion for research with pragmatism to drive actions and create impact for our customers.You have a passion for learning and growing by doing. We are here to help you. One of the preferred values of the team is: “High Challenge, High Support”!Excellent communication skills! We cherish our open and direct feedback culture in the team!INCLUSIVE BY DESIGNAt Zalando, our vision is to be the leading pan-European ecosystem for fashion and lifestyle e-commerce - one that is inclusive by design. We only assess candidates based on qualifications, merit, and business needs. We welcome applications from people of all gender identities, sexual orientations, personal expressions, racial identities, ethnicities, religious beliefs, and disability statuses. We only want to know why you're great for this role, so please avoid including your picture, age, and marital status in your CV as well.We want to provide you with a great candidate experience. Please feel free to inform us of any accommodations you may need, so we can best support and assist you throughout the hiring process.do.BETTER - our diversity & inclusion strategy: OUR OFFERZalando provides a range of benefits, here's an overview of what you can expect. Ask your Talent Acquisition Partner to learn more about what we offer.Employee shares program40% off fashion and beauty products sold and shipped by Zalando, 30% off Lounge by Zalando, discounts from external partners2 paid volunteering days per yearWork from abroad for up to 30 working days a year27 days of vacation a year to start for full-time employeesFamily services, including counseling and supportHealth and wellbeing options (including Wellhub, formerly Gympass)Mental health support and coaching availableDrive your development through our training platform and biannual peer-to-peer reviewRecruiterCasimir [email protected] note that all applications from this job page must be completed using the online form - we do not accept applications via e-mail. Once reviewed, our recruiters will contact applicants via an official Zalando email address (@zalando.de).In some cases we also work with a selection of headhunters and agencies to fill specific roles. Please note that neither Zalando nor our recruiting partners will ask for any kind of payment to apply for a job or attend an interview.If you have any questions about our recruitment process, please take a look at our .ApplyAbout ZalandoFounded in Berlin in 2008, Zalando is the leading European technology platform for fashion and lifestyle. Connecting 62 million active customers with more than 7,000 brands across 29 markets, our operations are built on a unique AI-powered data and infrastructure platform. For our customers, our multi-app approach - comprising Zalando, ABOUT YOU, and Lounge by Zalando - delivers an inspiring, highly personalized shopping experience, serving different customers with different needs. For our partners, we are building the operating system for e-commerce. Through ZEOS, Tradebyte, and SCAYLE, we open our logistics, software, and service capabilities to brands and retailers, enabling them to seamlessly scale their businesses across borders.Back to job descriptionApplied Scientist (all genders) - B2B Data Science and AlgorithmsThe ZEOS department is responsible for all partner-facing Zalando Logistics Solutions. We provide a holistic approach to delivering the fulfillment solutions that meet our partner’s needs by unifying these services under a single umbrella. We aim to provide our partners with a profitable fulfilment experience, and we see that to do this, Machine Learning, Operations Research and Data-driven solutions will play a pivotal role. We are seeking an Applied Scientist who is fueled by the desire to build innovative and impactful ML/Optimization systems for our B2B logistics partners. You will join an existing team of Applied Scientists, and Machine Learning Engineers and work in a cross-functional setup with Product Managers, Data and Software Engineers. The team’s focus is helping our B2B partners improve inventory health and order fulfilment efficiency. You will be building various ML/DL forecasting models (demand, returns, lead-times), stochastic inventory optimization solutions, recommendation services and emerging Agentic AI systems that generate these key insights for our partners and enable them to make data-driven decisions about their article assortment and inventory. You will play a key role in a cross-functional team!WHY YOU SHOULD BE INTERESTED…Shape the Product from Day One: You won't be a "model factory" stuck in a silo! You will be a core scientific partner, working directly with product and user research to define the problem, not just solve it. Your expertise will directly influence the product roadmap and identify new opportunities to create value for our partners.Solve High-Stakes, Complex Scientific Problems: This is your chance to go beyond standard forecasting. You'll be tackling cutting-edge challenges in stochastic inventory optimization, multi-echelon demand forecasting (including returns and lead-times), and building recommender systems for assortment planning. Your work will be at the absolute core of our partners' profitability.Pioneer Agentic AI & Robust Evaluations: As we explore opening up MCP servers to our partners, you will contribute to defining how we measure their performance/success in the user’s agentic journey. You will design and implement rigorous Agentic AI evaluation frameworks/metrics (e.g., LLM-as-a-judge, trajectory evaluation, and safety guardrails) to ensure the autonomous systems connecting to the MCPs act reliably and logically on behalf of our B2B partners.End-to-End Ownership: You will have the autonomy to see your ideas through from initial research and prototyping all the way to production. You will define the metrics for success, collaborate with engineers to deploy your models as scalable services, and monitor their real-world impact on partner KPIs.Drive Impact at a multi-merchant scale: Your solutions won't just help one partner. You will be building platform-level services that scale across hundreds of diverse partners/merchants, directly influencing millions of euros in merchandise value and shaping the future of a more sustainable and efficient e-commerce logistics network. WE’D LOVE TO MEET YOU IF…Educational background in a quantitative field - Masters degree or higher preferred.3+ years of hands-on industry experience in an Applied Scientist, Data Scientist, or Research Scientist role, applying scientific methods to solve business problems.Industry demonstrated knowledge and skills in at least one the following areas:Machine Learning or Deep Learning, particularly applied for time-series forecasting (e.g., LGBM, ARIMA, Prophet, Transformers, Nixtla, Darts,...)Machine Learning Engineering (e.g. service design, batch processing, GPU computing, git, docker, CI/CD, software testing)Operations Research and Optimization, (e.g., stochastic inventory models, linear/integer programming, Monte Carlo simulations, …)Agentic AI & MCP evaluation frameworksProficiency in SQL and experience working with large-scale datasets.Strong communication skills with the ability to explain complex scientific concepts to product managers and business stakeholders. A collaborative, product-oriented mindset and a desire to see your work create real-world impact.An entrepreneurial mindset, you value a diverse and fast-paced business environment, and hence balance your passion for research with pragmatism to drive actions and create impact for our customers.You have a passion for learning and growing by doing. We are here to help you. One of the preferred values of the team is: “High Challenge, High Support”!Excellent communication skills! We cherish our open and direct feedback culture in the team!INCLUSIVE BY DESIGNAt Zalando, our vision is to be the leading pan-European ecosystem for fashion and lifestyle e-commerce - one that is inclusive by design. We only assess candidates based on qualifications, merit, and business needs. We welcome applications from people of all gender identities, sexual orientations, personal expressions, racial identities, ethnicities, religious beliefs, and disability statuses. We only want to know why you're great for this role, so please avoid including your picture, age, and marital status in your CV as well.We want to provide you with a great candidate experience. Please feel free to inform us of any accommodations you may need, so we can best support and assist you throughout the hiring process.do.BETTER - our diversity & inclusion strategy: OUR OFFERZalando provides a range of benefits, here's an overview of what you can expect. Ask your Talent Acquisition Partner to learn more about what we offer.Employee shares program40% off fashion and beauty products sold and shipped by Zalando, 30% off Lounge by Zalando, discounts from external partners2 paid volunteering days per yearWork from abroad for up to 30 working days a year27 days of vacation a year to start for full-time employeesFamily services, including counseling and supportHealth and wellbeing options (including Wellhub, formerly Gympass)Mental health support and coaching availableDrive your development through our training platform and biannual peer-to-peer reviewRecruiterCasimir [email protected] note that all applications from this job page must be completed using the online form - we do not accept applications via e-mail. Once reviewed, our recruiters will contact applicants via an official Zalando email address (@zalando.de).In some cases we also work with a selection of headhunters and agencies to fill specific roles. Please note that neither Zalando nor our recruiting partners will ask for any kind of payment to apply for a job or attend an interview.If you have any questions about our recruitment process, please take a look at our .Apply