Scientific Data Product Manager - Nestlé Research

As a Scientific Data Product Manager at Nestlé Research in our Computational Sciences department, your mission will be to own the lifecycle of scientific data products across agriculture, packaging, clinical research, food safety, and health & nutrition scientific domains - reinforcing and increasing value of Nestlé Research's data foundations. You will identify high-value opportunities, translate scientific needs into impactful data products, and lead cross-functional delivery from discovery through adoption and communicate the same through an engaging product roadmap.

• Lead cross-functional work streams in a dynamic research environment, defining product vision, requirements, priorities, and success criteria that align science, data, and technology.
• Partner with scientific stakeholders to identify high-value needs, improve data quality, strengthen metadata, and make research data more discoverable and reusable through FAIR principles.
• Translate scientific needs into scalable data products across bioinformatics, clinical, and omics pipelines, from discovery through adoption.
• Orchestrate internal experts — including scientists, data domain owners, architects, data scientists, engineers, and software developers — together with external partners to accelerate priority delivery.
• Champion AI-enabled data products, using automation and AI to turn trusted, well-governed scientific data into faster, smarter insights.
• Own scientific data products for the long term, balancing feasibility and impact while using adoption, satisfaction, and delivered value to guide continuous improvement.

• Bachelors or Master’s or PhD in Bioinformatics, Life sciences, Chemistry or Computer Science, Engineering or equivalent, combining scientific domain understanding with data expertise.
• Significant professional experience driving the seeding, implementation, adoption and lifecycle management of data products in a scientific context, preferably in food, life sciences or pharma.
• Required expertise:
• Solid experience with Bioinformatics or Clinical or Omics research data domains
• Proven ability to identify business and scientific needs, perform stakeholder analysis, translate requirements into product backlogs, and prioritize delivery based on value, feasibility and strategic alignment.
• Strong product ownership skills, including roadmap management, backlog prioritization, user-centric design, value definition, testing strategies, adoption tracking and continuous improvement.
• Solid understanding of data engineering concepts, including data models, pipelines, ETL/ELT, APIs, metadata, data quality, data governance and analytics architectures, with the ability to guide technical decisions and challenge proposals.
• Understanding of modern data ecosystems - cloud platforms, data warehouse, (e.g., Snowflake), Linux and container technologies, orchestration tools (e.g., Airflow), Dev Ops practices.
• Nice-to-have expertise
• Semantic modeling, ontologies, and knowledge graphs (e.g., RDF/SPARQL, biomedical/chemical vocabularies).
• FAIR data implementation, metadata management, and lineage tooling
• Experience with reporting tools (e.g., DASH, Streamlit, Power BI) is an advantage
• Experience coordinating delivery through multidisciplinary internal teams and external partners.
• Ability to navigate complexity, manage ambiguity and align scientific, business and technical stakeholders around clear priorities.
• Experience in mentoring or guiding junior resources
• Excellent problem-solving, strong communication, stakeholder management and knowledge-sharing skills
• Fluent spoken and…