The posting
Does it excite you to apply machine learning across genomics, chemistry and imaging to shape how new medicines are discovered? And do you have experience evaluating, fine-tuning and deploying models that scientists actually use? If this sounds like you, and you are ready to take on a broad area of responsibilities, then you could be our new colleague.
Your new position As Director, Applied AI/ML and Statistics, Research, you will be Zealand Pharma’s expert in applying machine learning and statistical methods across biological, chemical and imaging data. You will decide which models and statistical methods are fit for which questions, adapt them to our data, and make them reliable and reusable across Research. You will lead translational data science projects that connect patient-derived multi-modal data with research and translational questions. You will also contribute to scientific and technology projects across our obesity and metabolic health pipeline.
We offer exciting responsibilities:
- Evaluate, benchmark, train and apply machine learning models across genomics, chemistry and histopathology data, fine-tuning foundation and task-specific models on internal data where this adds value
- Build and maintain a model catalog for Research that records available models, what each is validated for, its performance on internal benchmarks and how to access it
- Act as statistical subject matter expert for non-clinical experiments, advising on study design, power and analysis, and setting standards for how results are reported and interpreted
- Work with data science and IT teams to deploy, version and monitor models, establishing machine learning operations practices that make results reproducible and reusable
- Lead translational bioinformatics projects with Translational Medicine, Modeling & Simulation and Biometrics, analyzing multi-modal patient sample data to discover and validate biomarkers
- Contribute machine learning expertise to scientific and technology projects across Research, from target discovery to compound design
- Track external developments in machine learning for drug discovery and turn them into clear recommendations for project teams and leadership
Your profile You are a hands-on machine learning scientist with breadth across data types and the judgment to know when a model is good enough for a decision. You move easily between a benchmark, a biology question and an engineering conversation. You explain complex results simply to scientists and senior leaders, and you push back when the evidence does not support the claim. You thrive in a growing team where you can shape how models are chosen, shared and used across Research.
- PhD in machine learning, computational biology, statistics, cheminformatics or a related field, with 8+ years of applied machine learning experience in pharma, biotech or tech-bio
- Hands-on experience applying and fine-tuning models across more than one data modality, such as sequence and omics data, molecular structures and histopathology images
- Strong applied statistics for experimental science, including design of experiments, power analysis, mixed-effects and dose-response models, and multiple testing, with experience advising scientists on study design
- Experience in leading translational bioinformatics or clinical research projects, including biomarker analysis of transcriptomic or proteomic patient data
- Track record of rigorous model evaluation, including designing internal benchmarks and comparing open-source, commercial and in-house models
- Fluency in Python and deep learning frameworks such as PyTorch, and working knowledge of machine learning operations: experiment tracking, model versioning, containerized deployment, and cloud or high-performance compute
- Experience with peptide or biologic programs, metabolic disease, or clinical proteomics platforms such as Olink or SomaScan is an advantage
Your new team Research AI and Computational Sciences is part of Discovery Research. The team brings data science, AI and computational methods into Zealand Pharma’s research, from target discovery to candidate nomination. It works across our research sites in Copenhagen, Denmark and Cambridge, Massachusetts. It collaborates closely with chemistry, biology, modelling and simulation, clinical development, and Digital, Data & Technology. The position reports to the VP, Head of Research AI and Computational Sciences.
Let’s bond and be bold We have a unique culture, characterized by excellent teamwork and strong engagement across the organization. And we want you to help us strengthen this culture.
Making bonds is at the core of everything we do. From discovering and developing the best new peptide drugs for patients, to engaging with our partners or connecting with each other. We bond through curiosity and playfulness as a team, allowing ourselves to challenge common thinking and drive future innovation. Join us and let’s bond and be bold.
Interested in bonding with us? The applications will be reviewed as they come in. While a cover letter is not required, please include a few sentences in your CV explaining your motivation for applying.
We are committed to an inclusive recruitment process and welcome applications from all job applicants.
About Zealand Pharma A/S Zealand Pharma A/S (Nasdaq: ZEAL) is a biotechnology company focused on advancing medicines for obesity and metabolic health. Combining more than 25 years of peptide R&D expertise with a proprietary data platform that leverages advanced data-driven and AI/ML approaches, Zealand Pharma aims to lead a new era in obesity and metabolic health. To date, more than ten Zealand Pharma-invented drug candidates have entered clinical development, of which two products have reached the market and three candidates are in late-stage development. The Company has collaborations with global pharmaceutical and biotechnology partners for research, development, and commercialization. Founded in 1998, Zealand Pharma is headquartered in Copenhagen, Denmark, with a U.S. presence in Boston, Massachusetts. Learn more at www.zealandpharma.com
