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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - **Company:** Celonis - **Location:** Madrid, Spain - **Contract:** Permanent contract - **Skills:** Data Analysis, Software as a Service, Data Systems, Python (Programming Language), Machine Learning, Tensorflow, SQL Databases, Pytorch, Large Language Models, Deep Learning, Scikit Learn - **Published:** August 23, 2026 - **Apply:** https://www.adzuna.es/contact-us.html ## About the Role + Business-First Mindset: 6+ years of experience in data science or analytics, with a proven track record of driving direct business outcomes. + Pragmatic Approach: Strong technical foundations paired with the judgment to choose simple, practical tools over complex frameworks when appropriate. + High Autonomy: Ability to operate independently, proactively identify business gaps, and drive projects through to completion with minimal direction. + Technical Proficiency: Expertise in Python, SQL, and core ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch), with practical experience putting models into production. + Stakeholder Partnership: Ability to collaborate effectively with business units, build strong working relationships, and advocate for value-driven data solutions. + Nice-to-Haves: Experience in SaaS environments or building and deploying LLMs and generative AI workflows. ## Description We are seeking a pragmatic, proactive Senior Data Scientist to join our team. In this role, technical mastery is only half the equation - the real priority is translating data science into measurable business value. You will partner closely with business leaders to identify high-impact opportunities, select the right tool for the job (whether a simple logic rule or a complex machine learning model), and independently drive solutions from concept to production. If you are a self-starter who thrives on autonomy, prioritizes tangible business outcomes over technical complexity for its own sake, and enjoys shaping the data science discipline, we want to hear from you., + Drive Business Value: Partner directly with business stakeholders across Product, Engineering, Sales, and Marketing to identify opportunities and deliver measurable ROI through data science. + Pragmatic Problem Solving: Select and apply the right solution for the problem- prioritizing speed, simplicity, and practical business impact whether the optimal approach is simple heuristics or advanced machine learning. + End-to-End Ownership: Independently lead data science projects from initial scoping and data exploration through to deployment, monitoring, and value realization. + Model Development & Deployment: Build, validate, and maintain scalable analytics and machine learning solutions designed for real-world impact. + Shape the Discipline: Define best practices, methodologies, and tool selection strategy to build a mature, practical data science function at Make. + Cross-Functional Communication: Bridge the gap between technical rigor and business execution, translating complex models into clear, actionable insights for non-technical stakeholders. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) - [How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)](https://www.wearedevelopers.com/videos/752-how-we-built-a-machine-learning-based-recommendation-system-and-survived-to-tell-the-tale) ## Related Articles - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)