Develops and leads innovative computational strategies that accelerate drug discovery across a Research and Development portfolio from early discovery through clinical candidate nomination.
Compensation
The annual base salary we reasonably expect to pay is $218,200.00-$298,000.00. Individual pay decisions depend on various factors, such as primary work location, complexity and responsibility of role, job duties/requirements, and relevant experience and skills. In addition, this position offers an annual bonus with a target of 35% of the earned base salary and eligibility to participate in our equity based long term incentive program.
Responsibilities
Develops, implements, and enforces scientifically sound computational chemistry strategies that support drug discovery programs from target validation through lead optimization and candidate selection
Leads the application of molecular modeling, structure‑based drug design, ligand‑based design, and AI/ML approaches to enable compound design and optimization
Partners closely with medicinal chemistry, structural biology, pharmacology, and data science teams to integrate computational insights into project strategies and decision‑making
Evaluates, interprets, and communicates computational results, proactively providing strategic direction to project teams and senior management on compound design and program progression
Drives innovation through the evaluation and implementation of emerging computational technologies, including generative chemistry, predictive modeling, and large‑scale data analysis
Requirements
Bachelor's in chemistry or related field
Master's in computational chemistry or related field
PhD in computational chemistry, organic chemistry or a related field
In this role, you’ll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input.
Responsibilities
Design, evaluate, and critique chemistry and computational chemistry problems, workflows, and reasoning chains to support AI benchmarking initiatives.
Provide expert-level insights on reaction mechanisms, synthesis planning, spectroscopy analysis, molecular simulations, and electronic structure methods.
Assess and optimize experimental protocols, simulation parameters, and software configurations for accuracy and reproducibility.
Analyze chemical, spectroscopic, and simulation data and provide actionable recommendations.
Leverage domain knowledge to guide the development and validation of chemistry-related AI models and tools.
Requirements
PhD in chemistry, computational chemistry, chemical biology, materials science, or a closely related field.
Nice to have: Academic experience (postdoc, research scientist, faculty, or equivalent)
Nice to have: Peer-reviewed publications
Nice to have: Patents
Nice to have: Open-source scientific software contributions
A computational role focused on building models and workflows that transform raw analytical chemistry data (LC-MS) into labels for reactivity ML models and drug discovery.
Compensation
This role offers a base salary of $165,000-$180,000, along with meaningful equity ownership and competitive benefits.
Responsibilities
Automated interpretation directly from raw instrument output
Peak detection, product identification, adduct and fragment reasoning, feature annotation across the whole sample
Build agentic pipelines that process whole campaigns end to end
Own and manage the analysis pipeline: how raw data moves from instrument to label
Decide data quality, develop analytical QC policies, and record processing choices
Scientific Content Engineer contributing specialized expertise within a network of content management, data analysis, and domain experts to deliver accurate, timely, and high-quality content and data solutions.
Responsibilities
Apply scientific knowledge, established policies, approved technologies, and best practices to deliver accurate, timely, and high-quality content and data solutions
Translate business and user needs into effective deliverables that support projects, products, and services
Collaborate with scientific, operational, business, and technical stakeholders to understand requirements and support implementation of solutions
Support the development and refinement of evolving business priorities, operational, and technical requirements, including documentation, workflows, user stories, or other project artifacts
Contribute to cross-functional projects through analysis, documentation, testing, process improvement, and solution implementation activities
Requirements
Bachelor's degree in science or a technology-related domain
The Scientific Content Engineer contributes specialized expertise within a network of content management, data analysis, and domain experts to deliver accurate, timely, and high-quality content and data solutions.
Responsibilities
Apply scientific knowledge, established policies, approved technologies, and best practices to deliver accurate, timely, and high-quality content and data solutions
Translate business and user needs into effective deliverables that support projects, products, and services
Collaborate with scientific, operational, business, and technical stakeholders to understand requirements and support implementation of solutions
Support the development and refinement of evolving business priorities, operational, and technical requirements, including documentation, workflows, user stories, or other project artifacts
Contribute to cross-functional projects through analysis, documentation, testing, process improvement, and solution implementation activities
Requirements
Bachelor's degree in science or a technology-related domain
Lead digital transformation and automation projects in Fragrance S&T, designing data pipelines, deploying predictive models, and integrating AI tools to accelerate research and decision‑making.
Responsibilities
Lead digital transformation and evolution at Givaudan in Fragrance S&T
Lead digitalisation, automation and data science projects within Fragrance S&T
Integrate modern AI tools like LLM and predictive models (QSAR, QSOR) into existing and new applications
Scout and implement modeling tools facilitating scale up activities and other research opportunities
Align with corporate IT and collaborators to deploy solutions to production or other Givaudan infrastructure
Build, validate and deploy cheminformatics and machine learning methods and models to inform go/no-go across drug-discovery programmes.
Integrate with drug discovery programme teams to embed predictive models directly into decision-making and translate computational outputs into guidance that is accessible to non-experts.
Prototype exploratory, agent-driven workflows that combine LLM-based reasoning with cheminformatics tools, predictive models and experimental data to accelerate hypothesis generation, literature and data triage, and iterative design / make / test / analyse cycles.
Contribute to and validate production-quality code implementing state-of-the-art cheminformatics, computational chemistry and quantum chemistry methods that accelerate and improve decision-making on drug discovery programmes.
Prepare and present results of key validation experiments, details of capability builds, and developments on drug discovery projects to internal and external groups in a way that is both informative and accessible to the non-subject matter expert.
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