Applied ML Scientist

Skip to main contentThis website uses cookies to improve your web experience. By using the site, you agree to the use of cookies. Privacy PolicyDeclineAccept CookiesEnglishSign InApplied ML Scientist page is loadedApplied ML ScientistApplyremote typeHybridlocationsLisbon, Portugaltime typeFull timeposted onPosted 27 Days Agojob requisition idR34642Job DescriptionThe Enterprise Machine Learning team drives organizational value through scalable ML solutions and data-driven insights, fundamentally changing how business decisions are made. We collaborate closely with stakeholders, applying the latest advances in machine learning and statistical modeling to create highly impactful outcomes. Our commitment is to advance the state of applied science and robust system design to enhance and expand our core business capabilities.Role OverviewWe've spent the past year consolidating and validating our revenue data, most signals now live in one place. The next step is building a customer intelligence layer: systems that dynamically learn which signals drive outcomes, adapt as the business evolves, and surface insights that change how we act.As an Applied ML Scientist, you will be the person who makes that happen. You will train models, design experiments, and uncover the patterns that connect customer behavior to revenue outcomes.Then ship those insights as production systems that the business relies on daily. You own problems end-to-end: from formulating the right question, to training and validating models, to deploying them and measuring whether they actually moved the needle, in a closed feedback loop.You will work alongside a team of AI/ML Engineers, Data Engineers, and Analysts as part of the Enterprise Data & Analytics department. Your focus is the science: understanding what drives outcomes, building models that learn from data, and turning that understanding into systems that work.Key Responsibilities:Train, evaluate, and deploy models that predict and explain revenue-related outcomes (churn, expansion, conversion, engagement)Design and run experiments to establish relationships between customer signals and business resultsBuild production ML pipelines that learn continuously: ingest new data, retrain, validate, and serve predictions at scaleWork with large volumes of both structured data (usage metrics, revenue events, account attributes) and unstructured data (support tickets, conversations, product feedback)Use LLMs and deep learning where they're the right tool (embeddings, fine-tuning, text feature extraction)Define and track model performance in production: monitor drift, measure business impact, and iterate when models degradeOwn your models end-to-end from prototype through production deployment, monitoring, and maintenancePartner with product and engineering to embed intelligence where users make decisions.Translate model outputs into actionable insights for sales, success, and product teams: make the intelligence layer useful, not just accurateCollaborate with stakeholders to identify high-leverage questions and prioritize modeling work based on expected business impactThis Role Is For You If…You get more satisfaction from a simple model that ships and moves a metric than a complex one that scores well on a holdout setYou've trained models on real-world messy data and dealt with the unglamorous parts: label noise, class imbalance, feature leakage, data driftYou write production Python — tests, type hints, clean abstractions — not just notebooks with df_final_v3You think about problems in terms of "what will someone do differently because of this?" rather than "what's the most sophisticated technique?"You care that users actually change behavior because of your work: you're not done when the model is accurate, you're done when someone acts differentlyYou form opinions about what to build next based on data and user understanding, not just what's assigned to youWhat We Are Looking ForEducation & Experience3–5 years'...