Data Scientist 2

As a Data Scientist, you will work on some of the world's largest and most advanced services, leveraging data to improve user experiences, enhance service quality, and drive measurable impact at global scale. Apply data analysis, AI, and modelling techniques to understand user behaviour, identify opportunities, and inform product improvements. Write robust, reusable, and extensible code to support analysis, modelling, and operationalization at scale. Use AI-powered tools in your daily work to accelerate analysis, experimentation, and solution development. Develop and maintain metrics and evaluation frameworks to assess the quality, performance, and impact of services. Drive data-driven decision-making by presenting clear, actionable insights to senior leaders and stakeholders. Collaborate in a cross-functional environment with local and remote partners to deliver scalable, elegant, and impactful solutions. Acquire, prepare, and validate datasets for analysis and modelling, while contributing to best practices in data collection and data quality. Evaluate and improve existing products to ensure intelligent services evolve to meet user needs. Design, evaluate, and optimize prompts and fine-tuning strategies for large language models. Bachelor's, Master's, or Doctorate degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or a related field, with relevant experience in data science, such as working with structured or unstructured data, applying statistical methods, and communicating results; Software engineering experience covering software engineering best practices (code quality, repository hygiene, code reviews, unit testing, design documentation, continuous integration, and deployment). These requirements include but are not limited to the following specialized security screenings: Experience in customer-facing roles, project delivery, professional services, and/or consulting. Track record of defining, delivering, and optimizing exploratory data science and machine learning projects that address real-world problems. Knowledge of distributed big-data computing systems (e.g., Spark, Hadoop, Hive, Azure ML). Experience with experimentation frameworks and understanding how to measure end-user-impacting metrics. Experience with ML tools to build models and analyse data (e.g., Python, R, scikit-learn, TensorFlow, PyTorch, ML.NET). Experience with large language models (LLMs), including prompt engineering, fine-tuning, and evaluation for real-world applications.