Data Scientist

Job Description

Purpose Be integral part of the PS Data Science team that applies technical expertise in data management, data science, machine learning, artificial intelligence, and automation to design, build, deploy, and maintain solutions across multiple countries. Work with project teams and SMEs to understand business requirements and develop appropriate data and AI/ML solutions. Contribute solutions with explorative, predictive- or prescriptive models, utilizing optimization, simulation, and machine learning techniques to existing and new projects. Work independently to build applications for data collection and processing, exploration and visualization, analysis, regression, classification, and generation as required by the project and business teams. Accountabilities Develop and implement complex statistical models, machine learning algorithms, and data mining techniques to extract insights from large datasets. Lead data science projects from conception to completion, defining scope, methodology, and deliverables. Collaborate with business leaders to translate data insights into actionable strategies and recommendations. Guide and mentor junior data scientists, fostering their professional development and technical skills. Contribute to the design and improvement of data architecture, pipelines, and storage solutions. Stay current with the latest advancements in data science and introduce new techniques or technologies to the organization. Establish and maintain standards for data quality, documentation, and ethical use of data. Present complex findings to both technical and non-technical audiences, including executive leadership. Address complex business challenges using data-driven approaches and creative solutions. Improve the efficiency and scalability of data processing and model deployment. Develop deep knowledge in Syngenta s PS operations to better contextualize data insights. Own the design, build, and deploy process including collaboration with users and multi-disciplinary teams to fulfil the user, business and technical requirements. Required Knowledge & Technical Skills Bachelor s degree in Computer Science, Data Science, Engineering, Mathematics, or a related discipline (or equivalent practical experience). Understanding of ETL processes and data pipeline design. Required development skills: Data Science - TensorFlow Scikit-learn, SciPy, NumPy, Pandas, XGBoost, Keras, etc. Programming - Python with pytorch and tensorflow Data - SQL, EDA, Descriptive and Predictive analysis, visualization Preferred additional skills: Web services - RESTful APIs and API testing, JSON, etc. Front end - JavaScript, Flask, React, Node.js, Vue, Django, or other. Tools - Git, npm, pip, Heroku, or other tools. Knowledge and hands-on experience supervised and unsupervised ML using Logistic/multi-variate regression, Gradient Boosting, Decision Trees, Neural Network, Random Forest, Support Vector Machine, Naive Bayes, Time Series, Optimization, etc. Preference for proven experience in adapting algorithms to required models - Regression, Decision Trees, Random Forests, LLM. Preference for experience in production and supply domain: production planning, supply chain, logistics, track and trace, CRM, etc. Preference for experience in Deep Learning model development in agriculture, supply chain or related domains. Documentation of documentation of APIs, models, and operational manuals (Markdown, etc.). Must be able to work on end to end activities from design, development and deployment Required Experience Previous internship, placement, or project experience in data engineering, data science, software development, or a related field. Required 2 years experience of building data science projects using AI/ML models. Exposure to cloud platforms such as AWS, Azure, Google Cloud, or Data Bricks (preferred but not essential).