On behalf of the Alan Turing Institute I consulted with the pharmaceutical company Roche to understand how to make use of machine learning for prediction of non-small cell lung cancer (NSCLC). I was hired by the Turing to demonstrate expertise in the company and to secure a longer-term partnership. In a team of three, we had initial meetings to understand the client's needs, which ranged from integration of very large genomics datasets to understanding statistical biases in their data. I focused on developing a machine learning toolbox for survival analysis that could make survival-distribution predictions after being trained on their data. My work was released open-source as mlr3proba. As well as designing methods compatible with their data and research questions, I also identified biases in the data, discussed methods to prevent security breaches between datasets, and ran a benchmark experiment to highlight the potential for machine learning. The work was well-received and the client signed a long-term agreement with the Turing.