Want to know if this job is worth applying to?
Belas, Portugal
Hybrid
The 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.
We'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.
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 results
Build production ML pipelines that learn continuously: ingest new data, retrain, validate, and serve predictions at scale
Work 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 degrade
Own your models end-to-end from prototype through production deployment, monitoring, and maintenance
Partner 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 accurate
Collaborate with stakeholders to identify high-leverage questions and prioritize modeling work based on expected business impact
You get more satisfaction from a simple model that ships and moves a metric than a complex one that scores well on a holdout set
You've trained models on real-world messy data and dealt with the unglamorous parts: label noise, class imbalance, feature leakage, data drift
You write production Python — tests, type hints, clean abstractions — not just notebooks with df_final_v3
You 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 differently
You form opinions about what to build next based on data and user understanding, not just what's assigned to you
3–5 years' experience in applied machine learning, data science, or a related field
BA/BS in Computer Science, Statistics, Mathematics, or related quantitative discipline
Advanced degrees welcome but not required
Strong foundations in statistical modeling and machine learning: regression, classification, survival analysis, causal inference, uplift modeling, or similar
Experience training models on real-world datasets: feature engineering, validation strategy, handling messy data at scale
Comfort working with unstructured data (text, conversations) using embeddings, fine-tuning, or learned representations: you understand LLMs as modeling tools, not just API endpoints
Strong Python programming skills: you write production-grade code with tests, not just scripts
Strong SQL skills and experience with cloud data warehouses (Snowflake preferred)
Experience deploying and monitoring models in production (batch or real-time)
Nice-to-have: Experience with experiment design, A/B testing, and causal inference
Nice-to-have: Experience with orchestration tools (Airflow, dbt, or similar)
Nice-to-have: Experience with AI-assisted development workflows (Claude Code, Cursor, Copilot, or similar)
#LI-MK12
The intelligent heart of customer experience
Zendesk software was built to bring a sense of calm to the chaotic world of customer service. Today we power billions of conversations with brands you know and love.
Zendesk believes in offering our people a fulfilling and inclusive experience. Our hybrid way of working, enables us to purposefully come together in person, at one of our many Zendesk offices around the world, to connect, collaborate and learn whilst also giving our people the flexibility to work remotely for part of the week.
As part of our commitment to fairness and transparency, we inform all applicants that artificial intelligence (AI) or automated decision systems may be used to screen or evaluate applications for this position, in accordance with Company guidelines and applicable law.
Zendesk is an equal opportunity employer, and we’re proud of our ongoing efforts to foster global diversity, equity, & inclusion in the workplace. Individuals seeking employment and employees at Zendesk are considered without regard to race, color, religion, national origin, age, sex, gender, gender identity, gender expression, sexual orientation, marital status, medical condition, ancestry, disability, military or veteran status, or any other characteristic protected by applicable law. We are an AA/EEO/Veterans/Disabled employer. If you are based in the United States and would like more information about your EEO rights under the law, please click here.
Zendesk endeavors to make reasonable accommodations for applicants with disabilities and disabled veterans pursuant to applicable federal and state law. If you are an individual with a disability and require a reasonable accommodation to submit this application, complete any pre-employment testing, or otherwise participate in the employee selection process, please send an e-mail to peopleandplaces@zendesk.com with your specific accommodation request.
