AI/ML Multimodal Data Scientist

AI/ML Multimodal Data Scientist

dsm-firmenich

Delft, Netherlands

We are looking for an AI/ML Data Scientist to accelerate R&D across our health, nutrition, and beauty platforms by integrating and analysing multimodal biological and health data, enabling data-driven discovery and evidence generation for product innovation and claims.

This role will contribute to building a reusable R&D engine capable of systematically integrating internal and external multimodal datasets - spanning molecular/multi-omics, health, and lifestyle data - applying structured, reusable analytical frameworks and prioritising biologically plausible, health-relevant insights. Outputs from this work will directly inform discovery of healthy living and aging mechanisms, translate (pre)-clinical insights into product positioning hypotheses, and support claim substantiation.

This role sits within S&R Data Science & AI - Quantitative Science team and works closely with cross-functional scientific and innovation teams to support all Business Units within dsm-firmenich.

What You Will Do

You will contribute to building a digital R&D framework capable of:

• Systematically ingesting large-scale external datasets and linking the generated insights to relevant internal assets to accelerate evidence generation.
• Integrating diverse data types including diet, lifestyle, host omics, microbiome data, and clinical outcomes to identify actionable biological patterns and high-confidence intervention targets.
• Integrating biological clocks, molecular signatures, and health-related endpoints to quantify intervention effects and identify mechanistic pathways.
• Prioritising findings through AI/ML-assisted multi-criteria scoring frameworks that balance analytical robustness, biological plausibility, translational relevance, and novelty.
• Translating analytical results into structured scientific evidence packages that support product claims and commercial strategy.

Key Responsibilities

• Translate early-stage scientific and Business Unit questions into data-driven AI/ML-enabled analytical frameworks and projects that generate actionable targets and insights to guide strategic R&D platform priorities.
• Develop scalable pipelines for external data ingestion, harmonisation, and multi-modal integration.
• Leverage state-of-the-art AI tooling, including emerging agentic and generative AI approaches, to accelerate discovery, hypothesis generation, insight extraction, and interpretation of complex scientific results.
• Design and apply advanced AI/ML models to uncover non-linear relationships, generate hypotheses, and identify mechanistic links between interventions, host-microbiome biology, and health outcomes.
• Operate effectively in ambiguous environments, prioritising analyses under uncertainty and balancing scientific depth with decision-making timelines.
• Collaborate closely with Microbiome/Omics, Biostatistics, Trial Management, and Knowledge Management teams to build cross-functional analytical solutions and reusable assets.
• Lead and supervise internal and external contributors on analytical workstreams, ensuring methodological rigour, reproducibility, and alignment with project and business objectives.
• Continuously monitor advances in AI/ML and their application to nutrition and clinical research and actively bring forward new ideas and methodological innovations to strengthen the R&D portfolio.
• Communicate findings clearly to scientific, technical, and business stakeholders, contributing to evidence-based strategic decision-making.

What you bring

Education & Experience

• PhD in a quantitative discipline such as Data Science, Machine Learning, AI, Computational Biology, Bioinformatics, Systems Biology or related field, with a strong commercial mindset and preferably 3+ years of industry experience; or
• MSc with 5+ years of relevant industry experience in applied AI/ML within R&D or health-related domains.

Technical profile

• Demonstrated experience working with large-scale multimodal biological and health datasets, including identifying, assessing, and ingesting relevant sources such as human cohort studies, omics datasets, and domain-specific scientific repositories.
• Strong grounding in applied AI/ML for complex biological and health datasets, including longitudinal data structures, high-dimensional feature spaces, and robust model validation, with a demonstrated track record of applying these approaches in health, nutrition, biological, or clinical research contexts.
• Solid expertise in integrative analysis of host and microbiome omics data, with focus on downstream modelling and insight generation rather than primary bioinformatics processing.
• Experience integrating and harmonising cohort-based research datasets, including managing heterogeneous metadata structures and aligning variables across studies, is considered an advantage
• Familiarity with agentic AI systems, generative AI, LLM-based pipelines, and AI-assisted knowledge synthesis is considered an advantage.
• Strong coding skills in Python and/or R with emphasis on reproducibility, version control, modular pipeline development, and clear documentation.

Ways of working

• Proactive, independent, self-starter who can translate open-ended scientific and commercial questions into structured, scalable analytical proposals.
• Comfortable operating at the interface of AI, biology, and product innovation.
• Strong communicator capable of converting complex outputs into clear evidence narratives for scientific and business stakeholders.
• Systems-oriented, with the ability to think beyond one-off analyses toward reusable evidence-generation infrastructure.
• Curious, adaptable, and comfortable working in a fast-evolving, interdisciplinary environment.
• Experience collaborating with cross-functional stakeholders across different regions and time zones is considered an advantage.