Czym będziesz się zajmować? PROJECTWe are looking for an experienced Data Scientist to support the development and continuous enhancement of a large-scale data and machine learning ecosystem used in next-generation automotive solutions.The project involves building Spark-based data pipelines, improving data quality processes, implementing model evaluation workflows, and developing robust monitoring for ML models running in production.The role includes both initial development of the platform components and long-term maintenance and optimization.YOU WILLInitial Development:Develop Spark jobs for data ingestion and feature engineeringImplement data quality monitoring (metrics, dashboards, alerting)Build logic for model evaluation and automated deployment decisionsDevelop model monitoring with visualized KPIs and technical metricsFurther Development / Maintenance:Continuously extend data pipelines and feature engineering workflowsEnhance data quality metrics and monitoring coverageExpand model monitoring logic and dashboardsTroubleshoot and fix code issues, including edge casesExperiment with new ML algorithms and additional data attributesOptimize performance and cost (algorithms, data structures, storage formats)Adjust training/deployment pipeline configurations (frequency, resources, etc.)

Kogo poszukujemy? MUST HAVE5+ years of experienceStrong commercial experience with PySparkExcellent knowledge of PythonPractical experience with GitHubStrong data analysis skills (Jupyter, Seaborn, exploratory analytics)Solid SQL knowledgeExperience with Kubeflow or MLflow (MLOps frameworks for training, deployment & monitoring)Understanding of MLOps practices, including continuous trainingExperience with ML frameworks: scikit-learn, Pandas, OptunaAbility to create Grafana dashboardsGeneral knowledge of AWS services (S3, IAM, etc.)In-depth understanding of statistics and machine learning (missing data, outliers, model validation, algorithms)Fluent in Polish and good EnglishNICE TO HAVEExperience optimising data pipelines (Iceberg, Parquet, DynamoDB, etc.)Background in automotive or IoT data projectsExperience with cost optimisation for ML systemsExperience with large-scale model deployment pipelines