Senior Data Scientist (GenAI/ML) Location: Portugal - Remote/Hybrid options available (candidate has to be living in Portugal with NIF, NISS, and residency in place) Position Type: Full-time, Permanent Package: base salary, company card, performance bonus, meal card, monthly WFH allowance, Family Support Allowance, private healthcare and unlimited access to Udemy About Our Client We are recruiting on behalf of a premier, fast-growing international GameTech powerhouse that is pushing the boundaries of AI-driven entertainment and product optimization. Known for its high-performance tech stack and data-driven culture, our client is expanding its elite AI teams. They are looking for a Senior Data Scientist with a strong background in traditional Machine Learning and deep hands-on experience in Generative AI to take true ownership of the full model lifecycle. Role Overview As a Senior Data Scientist, your center of gravity will be turning complex business and product challenges into high-impact ML solutions. You will be responsible for the /"what/" and /"why/" of modeling - translating product requirements, designing advanced architectures, conducting rigorous validation, and shipping production-ready models. While you will collaborate closely with ML Engineers who build the underlying scalable platforms, you must possess the software engineering depth (Python/OOP) and MLOps fundamentals to ensure your models are successfully productionized, monitored, and optimized. Key Responsibilities & Deliverables - Model Design & GenAI Innovation: Architect, develop, and evaluate advanced ML models, with a specific focus on Generative AI applications, recommendation engines, and predictive systems. - End-to-End Lifecycle Ownership: Manage the full lifecycle of AI features, spanning from data collection and robust feature engineering to production implementation and post-launch optimization. - Rigorous Validation: Apply best-practice methodologies for dataset splitting, sampling, and validation to prevent data leakage and guarantee model integrity in live environments. - Production & MLOps Collaboration: Write clean, modular, production-ready code using Object-Oriented Programming (OOP). Design and maintain continuous model monitoring to proactively detect data drift or performance degradation. - Cross-Functional Leadership: Translate complex technical AI and deep learning concepts to product owners and business stakeholders. Mentor and guide junior team members on end-to-end projects. Technical Requirements & Must-Haves - Experience: 5+ years of proven experience building, validating, and deploying machine learning models in a live production environment. - GenAI Expertise: Demonstrated hands-on experience developing Large Language Model (LLM) applications, Prompt Engineering workflows, RAG systems, or fine-tuning deep learning models. - ML Theory: Expert knowledge of machine learning algorithms, deep learning frameworks, statistical modeling, and the underlying mathematical theory. - Software Engineering & Python: Advanced proficiency in Python and its core data ecosystem (Pandas, Scikit-learn, NumPy). Strong software engineering foundations with a heavy emphasis on OOP, testability, and modular code. - MLOps Fundamentals: Practical experience with containerization (Docker) and setting up or maintaining CI/CD pipelines for continuous model deployment and monitoring. - Communication: Exceptional English communication skills Nice-to-Have Qualifications - Experience with Big Data tools and cloud environments (e.g., Spark/PySpark, Azure, Databricks). Why Join This Team? This is an opportunity to work on highly visible AI products where your models directly impact millions of active daily users. You will join an environment that treats Data Science as a core business driver, providing access to massive datasets, top-tier cloud infrastructure, and a highly collaborative technical peer group.