R&D Data Modeling Manager Associate

Job title: R&D Data Modeling Manager Associate

Sanofi is a global life sciences company committed to improving access to healthcare and supporting the people we serve throughout the continuum of care. From prevention to treatment, Sanofi transforms scientific innovation into healthcare solutions, in human vaccines, rare diseases, multiple sclerosis, oncology, immunology, infectious diseases, diabetes and cardiovascular solutions and consumer healthcare. More than 110,000 people in over 100 countries at Sanofi are dedicated to making a difference in patients’ daily lives, wherever they live and enabling them to enjoy a healthier life.

As a company with a global vision of drug development and a highly regarded corporate culture, Sanofi is recognized as one of the best pharmaceutical companies in the world and is pioneering the application of Artificial Intelligence (AI) with a strong commitment to developing advanced data standards to increase reusability & interoperability and thus accelerate impact on global health.

The R&D Data Office serves as a cornerstone of this effort. Our team is responsible for cross-R&D data strategy, governance, and management. We partner with Business and Digital and drive data needs across priority and transformative initiatives across R&D. Team members serve as advisors, leaders, and educators to colleagues and data professionals across the R&D value chain. As an integral team member, you will be responsible for defining how R&D's structured, semi-structured and unstructured data will be stored, consumed, integrated / shared and reported by different end users such as scientists, clinicians, and more. You will also be pivotal in developing sustainable mechanisms for ensuring data are FAIR (findable, accessible, interoperable, and reusable).

Position Summary:

The primary responsibility of this position is to support semantic integration and data harmonization across pharmaceutical R&D functions. In this role, you will design and implement ontologies and controlled vocabularies that enable interoperability of scientific, clinical, and operational data. Your work will be critical in accelerating discovery, improving data reuse, and enhancing insights across the drug development lifecycle.

Main responsibilities:

• Develop, maintain, and govern ontologies and semantic models for key pharmaceutical domains, including preclinical, clinical, regulatory, and translational research

• Design and implement controlled vocabularies and taxonomies to standardize terminology across experimental data, clinical trials, biomarkers, compounds, and regulatory documentation

• Collaborate with cross-functional teams including chemists, biologists, pharmacologists, data scientists, and IT architects to align semantic models with scientific workflows and data standards

• Map internal data sources to public ontologies and standards to ensure FAIR (Findable, Accessible, Interoperable, Reusable) data principles

• Leverage semantic web technologies and ontology tools to build knowledge representation frameworks

• Participate in ontology alignment, reasoning, and validation processes to ensure quality and logical consistency

• Document semantic assets, relationships, and governance policies to support internal education and external compliance

Deliverables

• Domain-specific ontologies representing concepts such as drug discovery (e.g., compounds, targets, assays), preclinical and clinical studies, biomarkers, adverse events, pharmacokinetics / dynamics, mechanisms of action, and disease models built using OWL/RDF and aligned with public standards

• Controlled vocabularies & taxonomies for experimental conditions, cell lines, compound classes, endpoints, clinical trial protocols, etc.

• Semantic data models supporting the integration of heterogeneous data sources (e.g., lab systems, clinical trial data, external databases)

• Knowledge graphs or knowledge maps for semantic integration of structured data from internal R&D systems

• Mappings to public ontologies, standards, and external knowledge bases like: CDISC, MedDRA, LOINC, UMLS, SNOMED CT, RxNorm, UniProt, DrugBank, PubChem, NCBI

• Ontology documentation & governance artifacts, including ontology scope, design rationale, versioning documentation, and usage guidelines for internal stakeholders

• Validation reports and consistency checks, including outputs from reasoners or SHACL validation to ensure logical coherence and change impact assessments when modifying existing ontologies

• Training and stakeholder support materials: slide decks, workshops, and tutorials on using ontologies in data annotation, integration, and search

• Support for application developers embedding semantic layers

About you

• Experience: 5+ years of experience in ontology engineering, data management, data analysis, data architecture, or another related field

• Proven experience in ontology engineering, Proven experience in ontology development within the biomedical or pharmaceutical domain

• Experience working with biomedical ontologies and standards (e.g., GO, BAO, EFO, ChEBI, NCBI Taxonomy, NCI Thesaurus, etc.)

• Familiarity with controlled vocabulary curation and knowledge graph construction. Demonstrated ability to understand end-to-end data use and business needs

• Knowledge and/or experience of Pharma R&D or life sciences data and data domains. Understanding of FAIR data principles, data governance, and metadata management

• Strong analytical problem-solving skills.Demonstrated strong attention to detail, quality, time management and customer focus

• Excellent written and oral communication skills. Strong networking, influencing, and negotiating skills and superior problem-solving skills

• Demonstrated willingness to make decisions and to take responsibility for such. Excellent interpersonal skills (team player)

• Knowledge and experience in ontology engineering and maintenance are required.Knowledge and experience with OWL, RDF, SKOS, and SPARQL

• Familiarity with ontology engineering tools (e.g., Protégé, CENtree, TopBraid Composer PoolParty), Familiarity with ontology engineering methodologies (e.g., NeOn, METHONTOLOGY, Uschold and King, Grüninger and Fox, etc.)

• Knowledge and experience in data modeling are highly desired. Experience with pharma R&D platforms, requirements gathering, system design, and validation/quality/compliance requirements

• Experience with hierarchical data models from conceptualization to implementation, bachelor’s in computer science, Information Science, Knowledge Engineering, or related; Masters or higher preferred

• Languages: English