Job Description Syngenta Crop Protection Project and Portfolio Management (CP R&D PPM) is seeking a passionate and skilled Data Analyst/Scientist to join our dynamic Digital & Analytics team. This hybrid role in Data Analysis/Data Science places you at the forefront of data and AI-driven project and portfolio management, driving strategic decision-making, unlocking productivity and efficiency gains, and maximizing portfolio value creation. As a specialist in the PPM team, you will be instrumental in generating insights through project and portfolio data management and analysis. You will develop statistical, and AI/ML models based on current and historical data and enable scenario testing for predicted outcomes. Your role will support the use of portfolio and project management tools, enhance data quality, and extract value from diverse data sources. Additionally, you will contribute to digital transformation by applying new technologies and innovating data capture and analysis methodologies. Furthermore, you will leverage both business acumen and technological expertise to challenge the status quo of PPM decision analyses, fostering a culture of innovation and continuous improvement. If you are motivated by the opportunity to develop and support descriptive and predictive portfolio analytics workflows, collaborate with global stakeholders, and continuously improve data-analysis methods, this role is for you. Join us in shaping the future of sustainable and value-driven portfolio management at Syngenta. What We Are Looking For Advanced Degree in a relevant field (STEM, Data Science, Statistics, Computer Science, IT, etc.), or equivalent experience. Experience in project and portfolio management and analytics, preferably within a related industry (Agrochemical, Life Sciences, Pharma, etc.). In-depth Knowledge of data visualization, advanced analytics methods, tools, and programming languages (e.g., QlikSense, SQL, Python), probabilistic modeling (Monte Carlo, Efficient Frontier, etc.), and Data Science with exposure to cloud services. Machine Learning Experience is required; knowledge and experience in Natural Language Processing (NLP), Generative AI, and Large Language Models (LLMs) - including techniques such as Retrieval-Augmented Generation (RAG) and Prompt Engineering - is highly desired. Interest in New Technology, with critical thinking and a passion for cutting-edge data science algorithms in portfolio analytics, with a deep appreciation of the value and opportunities in predictive science and digital. Excellent Collaboration and Communication Skills across all levels of seniority and multiple geographies. Ability to Translate Complex Data into business-oriented value. Proactive and Autonomous work ethic, capable of regularly dealing with ambiguity, complexity, and uncertainty.