Senior Data Scientist

RESPONSIBILITIES Support definition and execution of analyses targeting innovation initiatives, development and implementation of methodologies, standards, and KPIs.  Prototype solutions and support pilot programs for R&D purposes, including trend analyses, representation/sampling, bias reduction, indirect estimation, data integration, automation, and generalisation.  Test-driven development of scalable data processing applications.  Deliver documentation of new methodologies and best practices.  Collaborate with an experienced team of Developers and Data Scientists.  Support various Operations teams as main users of our solutions.  Engage with stakeholders on scope, execution, data exchange, and outcomes for assigned projects.  Participate in multiple projects simultaneously.  ​​​​​​ Requirements: B.S., Masters, or PhD degree in Statistics, Mathematics, Social Science, Operation Research, or other hard sciences (e.g., Engineering, Computer Science, Biology, Physics, etc.) with outstanding analytical expertise and strong technical skills.  At least 3 years of relevant work experience.  Experience in trend analyses, multivariate statistics (parametric/non-parametric), sampling, optimization, bias reduction, indirect estimation, data aggregation techniques, automation, and generalization.  Good data visualization skills - ability to create clear and compelling charts and graphs  Proficient in Python programming language including data analysis and statistical packages (Pandas, Polars, NumPy, Scikit-Learn). Familiarity with Python standard library, especially unittest and argparse modules. Experience with Apache Spark or other big data processing solutions.  Experience with cloud computing and storage (MS Azure preferred).  Ability to manipulate, analyze, and interpret large data sources.  Strong communication/writing skills with good English  Preferred:  Experience in NIQ methodologies, data collection, platforms, research processes, and operations.  Experience in machine learning (appropriate application for prediction and classification)  Deep Learning - understanding of neural networks and deep learning frameworks like TensorFlow and PyTorch.  GenAI and AgenticAI - knowledge of available applications and programming packages for implementation of LLM based solutions and data processing automation.  Experience with the Docker and Linux command line.