ABOUT THE OPPORTUNITY Join aleading technology-driven gaming companyas aSenior Data Scientistand build production-grade machine learning models that power data-driven automation and personalized customer experiences for millions of users globally.This is asenior-level positionseeking experienced data scientists who can own thecomplete ML lifecycle from research through production deployment.
We're specifically looking for candidates fromproduct companies who understand what it means to build models that don't just work in notebooks but deliver real business value in production environments serving millions of users with high availability and performance requirements.The role combines strongmachine learning expertisewithsoftware engineering best practices- you'll translate business requirements into ML problems, perform exploratory data analysis and feature engineering, run comparative experiments for model training, and implement best practices on model selection and parameter tuning.
Working withincross-functional teamsthat include data scientists, ML engineers, and data engineers, you'll have the full skillset available to deliver end-to-end projects in anAgile/Scrum environment .
PROJECT & CONTEXT You'll be working within amachine learning team dedicated to making data-driven decisionsthat automate services while focusing on delivering tailored customer experiences.
The team builds avariety of models- from binary classification tasks and regression problems to sophisticated recommendation systems - covering a wide range of business sectors, utilizing different data types, and handling broad project diversity across the gaming platform.The work environment emphasizestransforming business needs into production applicationsrather than just academic research or proof-of-concepts.
You'll work with real-world constraints around latency, scalability, data quality, and business impact, requiring pragmatic approaches that balance model sophistication with operational requirements.
Thisend-to-end ownership- from understanding business problems through deploying and monitoring production models - is central to the role.Yourtechnical responsibilitiesspan the complete data science workflow.
You'll translate business requirements into well-defined machine learning problems, identifying the right problem formulation (classification, regression, ranking, etc.) and success metrics.Exploratory Data Analysis (EDA)andfeature engineeringform a significant part of your work - understanding data distributions, identifying patterns, handling missing values, creating meaningful features, and preparing datasets that enable effective model training.Experimentation and model developmentrequire running comparative experiments across different algorithms and approaches, implementing rigorous evaluation methodologies to ensure models generalize well, applying best practices in model selection based on problem characteristics and constraints, and conducting systematic parameter tuning to optimize performance.
You'll work extensively with thePython machine learning ecosystemincluding scikit-learn, pandas, NumPy, and various specialized libraries.Big data processingis handled throughSpark (PySpark), requiring you to design and implement data processing pipelines that can handle large-scale datasets efficiently, write PySpark code for distributed feature engineering and model training, and optimize Spark jobs for performance and resource utilization.
Yoursolid software engineering background in OOPensures your code is maintainable, testable, and follows engineering best practices rather than being one-off notebook scripts.Working inAgile/Scrum methodology, you'll participate in sprint planning, daily stand-ups, and retrospectives, collaborating closely with ML engineers who help operationalize your models, data engineers who build data pipelines, product managers who define requirements, and business stakeholders who use model outputs.
Strongteamwork, communication, and analytical thinkingskills are essential for navigating complex projects and making sound technical decisions.Core Tech Stack:Python (primary), PySpark (distributed processing), ML libraries (scikit-learn, pandas, NumPy) ML Focus:Binary classification, regression, recommendation systems, various supervised/unsupervised approaches Scale:Large-scale data processing, production models serving millions of users Methodology:Agile/Scrum with end-to-end project delivery Domain:Gaming/iGaming with diverse business applications and data typesWHAT WE'RE LOOKING FOR (Required)Senior Level Experience:This is aSENIOR position- we're seeking experienced data scientists, not junior or entry-level candidatesProduct Company Background:Strong preference for candidates from product companies(e.G., Farfetch, Talkdesk, Outsystems, Feedzai, or similar) who understand production ML at scaleEnd-to-End Experience:Proven track record workingE2E from research through production deployment- not just building models but seeing them through to real-world impactEducation:Background inComputer Science, Statistics, Mathematics, or related field(Master's or PhD advantageous)ML Algorithm Knowledge:Strong knowledge of machine learning algorithmsand respective theory - understanding when to apply different approaches and whyProduction Experience:2-8 years of hands-on experience delivering machine learning models to productionenvironments with real users and business impactPython ML Ecosystem:Deepknowledge of the Python machine learning ecosystemincluding scikit-learn, pandas, NumPy, matplotlib/seaborn, and specialized ML librariesPySpark Proficiency:Experience with Spark (PySpark)for distributed data processing and large-scale ML pipelinesSoftware Engineering Background:Solid software background in OOP- ability to write clean, maintainable, production-quality code following engineering best practicesBusiness Translation:Ability totranslate business requirements into machine learning problemswith appropriate problem formulation and success metricsEDA Expertise:Strong skills inexploratory data analysis- understanding data, identifying patterns, and deriving insightsFeature Engineering:Hands-on experience withfeature engineering- creating meaningful features that improve model performanceExperimental Design:Capability torun comparative experimentsfor model training with rigorous evaluation methodologiesModel Selection Best Practices:Knowledge ofbest practices in model selection, parameter tuning, and avoiding overfittingTeamwork:Strong teamwork skills- ability to collaborate with data engineers, ML engineers, and business stakeholders effectivelyCommunication:Excellentcommunication skillsfor explaining complex ML concepts to non-technical stakeholders and collaborating with technical teamsAnalytical Thinking:Stronganalytical thinkingabilities for problem decomposition and solution designAgile Experience:Working knowledge ofAgile methodologies and Scrum frameworkwith participation in agile ceremoniesLanguage:Fluency in English(B2+ minimum) both oral and written for international team collaboration and documentationLocation:Based in Portugal with availability for fully remote workNICE TO HAVE (Preferred)Azure/Databricks Experience:Hands-onexperience with Azure cloud platform and Databricksfor scalable ML workflowsDeep Learning:Knowledge of deep learningframeworks (TensorFlow, PyTorch, Keras) and neural network architecturesRecommendation Systems:Experience building recommendation systems- collaborative filtering, content-based, hybrid approaches, or modern neural recommendersAdvanced Spark:Deep Spark expertise including Spark MLlib, optimization techniques, and distributed trainingMLOps Practices:Experience with MLOps tools and practices (MLflow, model versioning, automated retraining, monitoring)Feature Stores:Experience with feature stores (Feast, Databricks Feature Store) for feature managementModel Deployment:Hands-on experience deploying models to production (REST APIs, batch processing, real-time inference)A/B Testing:Understanding of A/B testing methodologies and experimentation frameworks for model evaluationAdditional ML Domains:Experience with time-series forecasting, NLP, computer vision, or anomaly detectionSQL Proficiency:Strong SQL skills for data extraction and validation beyond PySparkData Visualization:Advanced data visualization skills (Plotly, Tableau, PowerBI) for insights communicationStatistics Advanced:Deep statistical knowledge beyond standard ML - causal inference, Bayesian methods, experimental designAutoML:Experience with AutoML tools and understanding when automated approaches are appropriateModel Interpretability:Experience with model explainability techniques (SHAP, LIME, feature importance analysis)Docker/Kubernetes:Understanding of containerization for ML model deploymentCI/CD for ML:Experience with CI/CD pipelines specifically for ML workflowsPerformance Optimization:Skills in optimizing model inference latency and computational efficiencyGaming Industry:Previous experience in gaming, iGaming, or high-transaction entertainment environmentsStreaming Data:Experience with real-time data processing and online learning scenariosGraph ML:Knowledge of graph neural networks or graph-based ML approachesReinforcement Learning:Understanding of RL concepts and applicationsPublications:Research publications in ML/AI conferences or journalsKaggle/Competitions:Strong performance in ML competitions demonstrating practical skillsOpen Source Contributions:Contributions to ML open source projects or librariesLocation:Portugal (100% Remote)#J-*****-Ljbffr