Principal Data Scientist

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At Bayer we’re visionaries, driven to solve the world’s toughest challenges and striving for a world where 'Health for all Hunger for none’ is no longer a dream, but a real possibility. We’re doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining ‘impossible’. There are so many reasons to join us.

If you’re hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there’s only one choice.

A Principal Data Scientist has complete oversight of data science within a platform. Individuals in this role will:

• Build capability
• Oversee resourcing, budgeting, professionalism and outputs and products
• Support and enable future IT developments
• Understand and use a wide range of data science techniques, tools and technologies
• Lead on ethics
• Communicate and present data science and data ethics effectively to senior leaders
• Champion the role of data science in supporting organizational priorities, and in collaborative working across platforms and functions
• Represent the platform on data science matters

This role will be located in Whippany, NJ. Candidates must live within reasonable commute to the Whippany, NJ site as remote working arrangements will not be accommodated.

• Identify opportunities to develop statistical insight, reports and models to support organizational objectives, while collaborating across the organization effectively;
• Use a variety of data analytics techniques (such as data mining and prescriptive and predictive analytics) for complex data analysis through the whole data life cycle;
• Use model outputs to produce evidence and help design services and policies;
• Understand a broad range of statistical tools, particularly those deployed within the organization, and can use these appropriately and help others to use them;

• Work with data engineers and data scientists to design and deliver products into the organization effectively;
• Understand the reasons for cleansing and preparing data before including it in data science products and can put reusable processes and checks in place;
• Access and use a range of architectures (including cloud and on-premise) and data manipulation and transformation tools deployed within the organization;

• Demonstrate practical knowledge of data science tools and techniques;
• Develop data science solutions that maximize insight;
• Identify opportunities for how data science can improve data practices;

• Demonstrate strong knowledge of knowledge graph and ontology concepts, including semantic modeling, graph-based data integration, reasoning, and metadata management;
• Apply semantic web standards and technologies, including RDF, OWL, and SHACL, to design, implement, validate, and govern enterprise ontologies and knowledge graphs;
• Use Generative AI and machine learning algorithms to accelerate ontology and knowledge graph development, including concept and relationship extraction, entity linking, taxonomy and schema generation, mapping, enrichment, and quality validation;
• Design human-in-the-loop processes to review, evaluate, and improve GenAI-generated semantic assets for accuracy, traceability, consistency, and business relevance;
• Collaborate with domain experts, data architects, data engineers, and AI engineers to integrate knowledge graphs and ontologies into scalable data and AI products;

• Champion the role of data…