Data Science Data Science Senior Data Scientist

VSNi has a vision “to see the challenges of global food production eradicated through the effective application of data, analytics and technology.” How we deliver our ambition is to develop and sell analytical software and data management solutions to support bio-scientists in many disciplines across the globe, be they in educational, not for-profit, or commercial settings. Our technical software and platform products are complemented by a growing consultancy and training proposition. Currently, commercial plant and animal breeders are our primary focus, but at the right time we expect to expand into other sectors such as retail or pharmaceuticals.   VSNi’s heritage stretches back over 20 years as one of the world’s foremost providers of statistical software to the biosciences. Now we are on a transformation journey to become a fully digitised ‘software-as-a-service’ vendor expanding our offering into data management and workflow optimisation as well as analytics. This means making our products available via cloud-computing to our customers, but also taking advantage of digital tools to deliver an enhanced online and offline customer experience, improving back-office activities to drive improved sales and marketing and customer support performance, growing the business in an effective, efficient, and highly measurable way.    Title: Senior Data Scientist We are seeking a highly skilled and research-oriented Senior Data Scientist (AI/ML) to design, build, and optimize scalable data pipelines and advanced analytical models. The ideal candidate will combine data engineering expertise, machine learning proficiency, and strong analytical thinking to drive innovation in Agricultural Technology, Horticulture, and Drug Discovery domains. You will collaborate closely with data scientists, product managers, and engineering teams to translate complex data challenges into actionable, data-driven solutions that power AI-enabled decision systems and predictive intelligence platforms. We value individuals with a proven track record of delivering results across frontend, backend, automation, and ML domains. Strong leadership and people management skills are essential, as you'll mentor others and help shape the technical culture of the organization.   Research, design, and develop statistical learning and deep learning models for complex data analysis and predictive modeling. Implement and validate advanced AI/ML algorithms in domains such as crop health analysis, molecular discovery, or environmental modeling. Evaluate and adopt emerging AI/ML frameworks and techniques relevant to company R&D goals. Data Engineering & Infrastructure Architect and maintain robust ETL/ELT pipelines for structured and unstructured data from multiple sources (sensors, experiments, IoT, genomic datasets, etc.). Optimize data storage, processing, and retrieval systems to support AI model training and deployment. Ensure data integrity, security, and quality across the machine learning lifecycle. Translate business objectives into machine learning use cases and actionable research strategies. Communicate model outcomes, insights, and implications effectively to both technical and non-technical stakeholders. Stay abreast of the latest research in AI, ML, NLP, and data science. Drive ethical, transparent, and responsible use of AI in all solutions. Lead initiatives and ensure timely delivery of assigned tasks with high quality   Required Experience:     Proficiency in Python and R for data analysis, model development, and automation. Strong background in machine learning, deep learning, and statistical modeling (linear/logistic regression, decision trees, clustering, etc.). Experience with TensorFlow, PyTorch, Scikit-learn, or Keras frameworks. Expertise in SQL and working with relational and NoSQL databases. Strong knowledge of data preprocessing, feature engineering, and data quality management. Hands-on experience with data analytics tools and visualization platforms. Familiarity with NLP, predictive modeling, and AI-driven analytics. Bachelor’s or Master’s degree in Computer Science, Data Engineering, Applied Mathematics, Statistics, or a related field. Master’s degree in data science, Computational Biology, or AI/ML (mandatory).    Experience or research exposure in AgriTech, Horticulture, Biotechnology, or Drug Discovery. Understanding of biological datasets, environmental variables, or scientific data analysis. Exposure to AI/ML applications in sustainability, crop yield optimization, or molecule screening. Opportunity to work at the intersection of AI research and scientific innovation. Collaboration with domain experts in agriculture, life sciences, and AI research. Access to cutting-edge cloud infrastructure and research datasets. A culture that values experimentation, ethics, and data-driven innovation. Opportunities to innovate in AI-driven, data-centric, and cloud-native products   Remote working