Move comfortably between Python/data engineeringandnbsp;- modelling/optimizationandnbsp;- Back Endandnbsp;- cloud/deployment - client discussions.The defining trait is the combination of genuine engineering depth with the maturity to sit in front of clients, translate evolving stakeholder needs into features, and drive delivery.Strong Python for data and Back End work - Pandas plus optimization/scientific libraries (eg Pyomo, Scikit-Learn)Building simulation, forecasting, and KPI/analytics frameworks to model financial or operational outcomesData engineering: ingestion and transformation pipelines for time-series/market data, schema design, automated validation/testing (eg pytest)Interactive dashboarding (eg Plotly Dash, Bokeh, or similar) for decision supportCloud and deployment: AWS, Docker, CI/CD across dev/QA/prod; message queues (eg RabbitMQ) a plusDatabases: PostgreSQL, plus exposure to NoSQL (eg Cassandra) and OracleOwns client relationships and runs the cadence (regular working sessions, requirements gathering, alignment)Scopes loosely defined problems into deliverable workstreams with minimal directionCan technically lead on a delivery teamSets engineering standards (testing, deployment hygiene, code quality)Working with non-technical stakeholders, problem framing mindset.Advanced degree in a quantitative field (statistics, analytics, computer science, engineering or similar)Able to collaborate across European and North American time zonesEnglish essential; ..... full job details .....