About the role
Data Scientist - Marley Spoon This website uses cookies to ensure you get the best experience. Marley Spoon and our selected partners use cookies and similar technologies (together “cookies”) that are necessary to present this website, and to ensure you get the best experience of it. If you consent to it, we will also use cookies for analytics purposes. See our Cookie Policy to read more about the cookies we set. You can withdraw and manage your consent at any time, by clicking “Manage cookies” at the bottom of each website page. Accept all cookies Decline all non-necessary cookies Cookie preferences Select which cookies you accept On this site, we always set cookies that are strictly necessary, meaning they are necessary for the site to function properly. If you consent to it, we will also set other types of cookies. You can provide or withdraw your consent to the different types of cookies using the toggles below. 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Portugal remote-first, Lisbon days optional. Ship models end-to-end and drive real impact—apply now! Apply for this job Help Marley Spoon use data and machine learning to improve personalization, forecasting, and decision-making—supporting better customer experiences and less food waste. This role is remote within Portugal , with optional collaboration days in our Lisbon office . About Marley Spoon Marley Spoon is a food-tech company focused on making home cooking easier, more enjoyable, and more sustainable. Since 2014, we’ve grown into a global meal kit platform serving customers across six countries . Behind every delivery is a set of systems that help us personalize experiences, plan efficiently, and continuously improve quality. Our Data Tribe is central to building those systems through models, analysis, and experimentation. Role Mission As a Data Scientist in the Data Tribe, you’ll develop and deploy models that connect directly to business outcomes. You’ll work cross-functionally with Product, Engineering, Marketing, and Operations —owning your work end-to-end from problem framing to production monitoring. What You’ll Deliver You’ll contribute across several model-driven areas: Personalization & recommendations Build and iterate recommendation and personalization models that tailor meals, recipes, and content to customer preferences and behavior. Improve relevance through strong evaluation, offline/online testing, and feedback loops. Forecasting, planning & waste reduction Develop and maintain time-series forecasting and planning models to improve demand prediction, logistics decisions, and food waste reduction. Create clear model assumptions and performance reporting so partners can trust and use outputs. Retention & lifecycle analytics Support retention and marketing initiatives via churn prediction, CLV modeling, and campaign optimization. Translate model outputs into decisions and measurable interventions. Experimentation & measurement Help design and analyze A/B tests and experiments with robust statistical practices. Enable teams to make decisions backed by evidence (not intuition), including effect sizing and uncertainty. New methods where they add value Explore and apply newer approaches (e.g., LLMs, generative AI) when they solve real problems—working closely with product and engineering. End-to-end model ownership Take models from exploration and feature engineering through validation, deployment (with engineering partners), and monitoring. Collaborate with other Data Tribe members on reviews, knowledge sharing, and improving DS practices. What Success Looks Like (first 6–12 months) You’ve shipped at least one model into production and can demonstrate how it supports a core metric or business outcome. Recommendation quality or personalization performance improves through measurable iteration and experimentation. Forecasting/decision-support work leads to clearer planning or operational improvements. You’re a reliable partner to cross-functional teams—known for clarity, rigor, and ownership. About You Must-haves ~ 3+ years experience in applied data science (or similar). Strong fundamentals in machine learning, statistics, and time-series modeling (recommender experience is a plus). Proficient in Python and SQL , with hands-on use of ML libraries (e.g., scikit-learn, PyTorch, XGBoost). Experience with modern data tooling and platforms (e.g., Snowflake, Looker, Airflow , or similar). Ownership mindset: you care about reliability, maintainability, and real-world impact. Able to explain complex concepts simply to non-technical stakeholders. Nice-to-haves Experience in subscription, e-commerce, or consumer product companies. Prior work in personalization, recommendations, or customer lifecycle/retention topics. Exposure to LLMs / generative AI in product contexts. Experience in Agile, cross-functional product teams. If you don’t meet every item but feel you can grow into