We are seeking a highly skilled Data Scientist specializing in massspectrometry-based proteomics to join our data science team. In this role, you will leverage advanced analytical techniques to extract meaningful insights from complex proteomics datasets generated by state-of-the-art mass spectrometry techniques. You'll play a crucial role in accelerating our molecular glue discovery platform by developing robust data science workflows that bridge high-throughput screening data with biological understanding.

• Proteomics Data Analysis: Analyze large-scale DIA and DDA shotgun-proteomics datasets to identify differential expression patterns and elucidate molecular mechanisms
• Algorithm Development: Design and implement algorithms and statistical models to process, quality control, and interpret complex proteomics data
• High-Throughput Screening Support: Develop automated pipelines for analyzing LC-MS data from high-throughput screening campaigns to identify novel molecular glue targets and mechanisms
• Data Integration: Integrate proteomics data with other omics datasets, chemical structure data, and biological pathway information to generate actionable insights
• Visualization & Reporting: Create data visualizations and comprehensive reports for cross-functional teams including medicinal chemistry, biology, and clinical development
• Method Development: Collaborate with analytical chemistry teams to optimize data acquisition and develop computational approaches for proteomics data analysis
• Platform Enhancement: Contribute to the continuous improvement of our molecular glue discovery platform through innovative data science methodologies

• PhD or MS in Data Science, Computational Biology, Bioinformatics, Physics, Chemistry, or a related quantitative field, with publications in computational proteomics, chemoproteomics, or chemical biology.
• A minimum of 2+ years industrial hands-on experience in proteomics data analysis
• Knowledge of SQL and R, or other data analysis tools
• Proficiency in Python programming with experience in data science libraries (pandas/polars, numpy, scipy, scikit-learn, matplotlib/seaborn/plotnine/ggplot)
• Experience with cloud computing platforms (AWS, GCP) and containerization technologies
• Demonstrated experience working with LC-MS or other omics data, particularly in high-throughput screening (HTS) environments
• Strong foundation in core data science concepts including statistical analysis, machine learning, data visualization, and experimental design
• Strong foundation in mass spectrometry data processing software algorithms (identification, quantification, missing value imputation, differential expression)