Data Scientist Senior

features to industrialise machine learning and optimisation models in Python using best-practice software principles (e.g., strict typing, classes, testing). Building automated, robust data cleaning pipelines that follow software best practices (in Python). Implementing integrations between the core algorithm (machine learning or optimisation) and a workflow orchestration paradigm such as Dagster. Implementing software in a cloud-based deployment pipeline with Continuous Integration / Continuous Deployment (CI/CD) principles. Building logging, error handling, and automated tests (e.g., unit tests, regression tests) to ensure the robustness of operationally critical decision-support products. Delivering features to harden an algorithm against edge cases in the operation and in data. Conducting analysis to quantify the adoption and value capture from a decision-support product. The Data Scientist is also accountable for ways of working fit for an Agile cross-functional development squad

including: Using Git versioning best practices for version control. Strong knowledge of either machine learning and optimisation techniques, incl. Fluent in Python (required) and other programming languages (preferred) with strong skills in applying DS, ML, and OR packages (scikit-learn, pandas, numpy, Gurobi etc.) Proficient in working with cloud platforms (AWS preferred), code versioning (Git), experiment tracking (e.g., Experience with cloud-based ML tools (e.g. SageMaker), data and model versioning (e.g. GitHub Actions), workflow orchestration (e.g. Airflow/Dagster) and containerised solutions (e.g. Docker, ECS) nice to have. Experience in code testing (unit, integration, end-to-end tests). Advanced analytical skills, including the ability to apply a range of data science and analytic techniques to quickly generate accurate business insights. Understanding of the trade-offs of different data science, machine learning, and optimisation approaches, and ability to intelligently select which are the best candidates to solve a particular business problem. Master's degree or greater in data science, ML, or operational research, or 2+ years of highly relevant industry experience (required). 0-2 years working on production ML or optimisation software products at scale (required). Experience in developing industrialised software, especially data science or machine learning software products (preferred). Experience in relevant business domains (transportation, airlines, operations, network problems) (preferred). Opportunity for professional development in a technological and innovative international environment. You will be part of a pleasant and challenging work environment, surrounded by colleagues who will support you in overcoming challenges within the projects where you will grow. We provide professional growth opportunities with individualized career plans aligned with training and certifications covered by the company, designed for your professional and personal development. Flexible compensation (health insurance, transportation vouchers, restaurant vouchers, and childcare vouchers) and access to a network of gyms and sports centres at a special rate through the company.