AI/ML Multimodal Data Scientist (m/w/d)

dsm-firmenich

8d

About The Position

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.
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.

Requirements

• 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.
• 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.
• Strong coding skills in Python and/or R with emphasis on reproducibility, version control, modular pipeline development, and clear documentation.
• 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.

Nice To Haves

• 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.
• Experience collaborating with cross-functional stakeholders across different regions and time zones is considered an advantage.

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.

Benefits

• A central role in shaping the organisation’s AI-driven target identification and prioritisation capabilities.
• High degree of autonomy to define analytical approaches and propose new data-driven initiatives within a fast-evolving innovation landscape.
• Close collaboration with innovation and commercial R&D teams, enabling tangible societal and commercial impact
• Access to cutting-edge internal and external datasets and leading (internal & external) scientific expertise.
• A translational environment where insights move from data to mechanisms to product hypotheses and claim strategies.
• A collaborative, learning-focused environment across scientific and technical teams.
• Career development within a global, purpose-driven organisation.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree