Principal Data Scientist Remote Portugal

ABOUT THE OPPORTUNITY

Join a leading technology-driven gaming company as a Principal Data Scientist and become a key technical leader shaping the future of AI-powered experiences in the iGaming industry. This position offers the rare opportunity to work at the forefront of AI innovation in one of the most dynamic, data-rich industries globally, while enjoying 100% remote work from anywhere in Portugal.

This is a high-impact technical leadership role where you'll architect cutting-edge AI applications that create genuine competitive advantage and deliver breathtaking experiences to millions of players worldwide. You'll tech-lead the AI tribe, mentor fellow data scientists, and define the company-wide AI technical and product roadmap - balancing strategic vision with pragmatic, impact-driven prioritization that resists AI hype and focuses on measurable business value.

The position is ideal for tenured AI/ML experts with 8+ years of hands-on experience and PhD-level expertise in specialized domains like Recommendation Systems, NLP, Computer Vision, or Reinforcement Learning. You'll work autonomously on high-complexity projects with end-to-end ownership from ideation through operationalization and maintenance, leveraging large-scale data to build state-of-the-art models that power a global gaming platform serving millions of users in real-time.

PROJECT & CONTEXT

You'll be leveraging data and AI at massive scale to deliver value across multiple product areas in the iGaming ecosystem - from personalized content recommendations and player behavior prediction to real-time anomaly detection, natural language interfaces, and intelligent automation. Working with one of the richest datasets in the entertainment industry, you'll build and deploy ML/DL models that process billions of events, optimize player experiences, and drive critical business outcomes.

As Principal Data Scientist, your responsibilities span the complete AI leadership spectrum. On the technical side, you'll architect scalable, reliable, and high-performance AI applications along with their corresponding ML/DL algorithms, considering both algorithmic sophistication and production engineering constraints. You'll tech-lead the AI tribe to deliver state-of-the-art models in production - not just proof-of-concepts, but battle-tested systems that operate reliably under real-world conditions with millions of concurrent users.

Your strategic responsibilities include defining the AI technical and product roadmap aligned with business objectives, making architectural decisions that will scale the platform for years to come, and enabling impact-driven prioritization through quick, impactful PoCs that demonstrate tangible value. You'll resist AI hype by focusing on solutions that genuinely move business metrics, conducting rigorous experiments to validate approaches before committing resources to full implementation.

The role emphasizes autonomy and end-to-end ownership - you'll drive projects from initial ideation through research, development, deployment, and ongoing maintenance. This includes deep involvement in MLOps practices, ensuring your models aren't just scientifically sound but operationally robust with proper monitoring, versioning, testing, and continuous improvement processes. You'll make critical trade-off decisions throughout the ML development lifecycle, optimizing for the right balance of accuracy, latency, resource efficiency, and maintainability.

Mentorship and technical leadership are central to the role - you'll inspire and guide fellow data scientists, fostering both technical depth and soft skills growth across the team. You'll establish engineering excellence standards, promote best practices, and create an environment where innovative AI research translates into production impact. Your communication skills will be tested daily as you engage with diverse audiences from business stakeholders to cross-functional technical teams, providing guidance and translating complex AI concepts into actionable insights.

The technical environment leverages Python as the primary language with the full modern ML/AI stack including deep learning frameworks, distributed computing for large-scale data processing, and comprehensive MLOps tooling for production deployment. You'll stay ahead of the AI curve, continuously evaluating and applying emerging technologies - particularly in areas like generative AI, agentic AI, and foundation models - to enhance capabilities and maintain competitive advantage in a rapidly evolving industry.

Core Tech Stack: Python (primary), ML/DL frameworks (TensorFlow, PyTorch), MLOps platforms
Scale: Billions of events, millions of concurrent users, real-time inference at global scale
AI Domains: Recommendation Systems, NLP, Computer Vision, Time-series, Reinforcement Learning
Architecture: Production-grade AI systems, microservices, distributed computing
Impact: Direct influence on product roadmap and technical strategy across entire AI organization

WHAT WE'RE LOOKING FOR (Required)

• Extensive Python Experience: 8+ years of hands-on experience leveraging large-scale data to build ML/DL models with Python
• PhD-Level Expertise: Deep, research-grade expertise in one or more specialized AI domains: Recommendation Systems, Natural Language Processing, Computer Vision, Speech and Audio Processing, Time-series Analysis, Reinforcement Learning, or Graph Learning
• ML Lifecycle Mastery: Deep understanding of the complete ML development lifecycle and how to optimize trade-offs at each step - from problem formulation and data preparation through model training, evaluation, deployment, and monitoring
• Production AI Track Record: Proven track record of designing, implementing, optimizing, and scaling performant AI-empowered applications that operate reliably in production environments
• MLOps Expertise: Solid experience with MLOps practices for operationalizing and maintaining AI applications in production - versioning, monitoring, continuous training, A/B testing, and incident response
• Architecture Skills: Ability to architect scalable, reliable, and high-performance AI applications considering both algorithmic sophistication and engineering constraints
• End-to-End Ownership: Demonstrated autonomy on high-complexity projects with ownership from ideation through operationalization and maintenance
• Technical Leadership: Experience tech-leading AI teams or initiatives, driving state-of-the-art models from research to production
• Strategic Thinking: Ability to define technical roadmaps, prioritize initiatives based on impact, and resist AI hype in favor of pragmatic value delivery
• Quick PoC Validation: Skill in designing and executing quick, impactful proof-of-concepts that demonstrate value and inform strategic decisions
• Mentorship Capability: Proven ability to inspire and mentor data scientists, fostering both technical and soft skill growth
• Cross-Functional Communication: Excellent communication and collaboration skills targeting diverse audiences - from business stakeholders to technical teams, explaining complex AI concepts clearly
• Technical Guidance: Ability to provide technical guidance and insight across organizational levels, influencing architectural decisions and best practices
• Trade-Off Optimization: Deep understanding of how to optimize various trade-offs - accuracy vs. latency, model complexity vs. interpretability, development speed vs. production robustness
• Innovation Focus: Commitment to staying ahead of the AI curve, continuously evaluating and applying emerging technologies to enhance capabilities
• Language: B2+ English level (Upper Intermediate minimum) for technical communication, documentation, and presentations
• Location: Based in Portugal with availability for fully remote work

NICE TO HAVE (Preferred)

• Generative AI Expertise: Deep expertise in genAI or agentic AI systems with understanding of latest architectures, techniques, and applications
• Prompt Engineering: Hands-on experience in prompt design, optimization, testing, and evaluation for different types of foundational models (GPT-style, Claude, Gemini, etc.)
• Foundation Models: Experience fine-tuning, deploying, and serving large language models or other foundation models in production
• RAG Systems: Understanding of Retrieval-Augmented Generation architectures for grounding LLM outputs in proprietary knowledge
• Agentic AI: Experience building AI agents that can plan, use tools, and operate autonomously to accomplish complex tasks
• Apache Spark: Familiarity with Apache Spark for distributed data processing and large-scale ML pipelines
• Delta Lake: Experience with Delta Lake for reliable data lakes with ACID transactions and time travel
• Stream Processing: Knowledge of Apache Kafka or Apache Flink for real-time data streaming and processing
• NoSQL Databases: Hands-on experience with NoSQL databases (MongoDB, Cassandra, Redis) for appropriate use cases
• Big Data Ecosystem: Broader familiarity with big data technologies and architectures for handling petabyte-scale datasets
• Deep Learning Advanced: Expertise with advanced deep learning architectures - transformers, attention mechanisms, GANs, diffusion models
• Model Optimization: Experience with model compression, quantization, distillation, or other optimization techniques for efficient deployment
• AutoML: Understanding of automated machine learning approaches and when to apply them
• Causal Inference: Knowledge of causal inference methods for understanding true cause-and-effect relationships
• Experimentation Platforms: Experience building or using experimentation platforms for A/B testing and multi-armed bandit algorithms
• Feature Stores: Hands-on work with feature stores (Feast, Tecton) for feature management and serving
• Model Serving: Deep knowledge of model serving platforms (TensorFlow Serving, TorchServe, Triton, KServe)
• Kubernetes: Experience deploying ML workloads on Kubernetes with proper resource management
• Cloud Platforms: Expertise with cloud ML services (AWS SageMaker, Azure ML, Google Vertex AI)
• Gaming Industry: Previous experience in gaming, iGaming, sports betting, or entertainment technology
• Real-Time Systems: Experience with low-latency, real-time ML inference systems serving high-throughput requests
• Recommendation Systems Advanced: Deep expertise in modern recommendation algorithms - collaborative filtering, content-based, hybrid, sequential, neural collaborative filtering
• NLP Advanced: Cutting-edge NLP experience beyond standard approaches - few-shot learning, multilingual models, domain adaptation
• Computer Vision Advanced: Advanced CV expertise - object detection, segmentation, video understanding, generative models
• Reinforcement Learning Production: Rare experience deploying RL systems in production environments with real-world impact
• Publications: Research publications in top-tier conferences (NeurPS, ICML, ICLR, CVPR, ACL) or peer-reviewed journals
• Open Source Contributions: Active contributions to major ML/AI open source projects or frameworks
• Technical Speaking: Experience presenting at conferences, meetups, or internal tech talks

Location: Portugal (100% Remote)