Essential skills:

• understand and process data sources of lab experiments with various readouts - explore, clean and featurize datasets on RNASeq, CRISPR, single cell and spatial techniques
• familiarity with omics data processing
• fundamental ML knowledge - statistical modeling basics, train/test split, bias/variance tradeoff, model evaluation metrics
• hands-on experience with dataset processing for analysis and ingress of various training data sources (in vitro, in vivo, PDX, patient)
• featurization of raw data for various ML models - e.g. find out what are the descriptive features that define phenotype after a gene knock-out on a cell line
• solid coding skills for processing large datasets (Python is preferred, but not a must-have. Can bring your strongest language.)
• process research papers and pick up new techniques and concepts to be applied in the team's daily work
• eager to proactively contribute to the planning of our research roadmap. You'll have an impact on defining our next step
• work independently and also as part of a team – carry out tasks that are going to contribute to the whole

Nice to have:

• PhD in biology, bioinformatics, computational biology or related field
• experience in industrial work settings
• experience with interpretation techniques of ML models
• train basic black box ML models as part of benchmarking
• evaluation of cell models with a biology-first mindset. E.g. what is a good model from a network biology point of view?

You are a Turbiner if these personality traits apply to you

• You have self-awareness and strong intention on personal growth
• You seek out feedback and learn from mistakes and apply what you learned
• You take responsibility and focus on the outcomes over tasks
• You focus on shared success and are open to challenging others respectfully to get the best possible outcome
• You are in it for the long haul and have passion for our mission