The training in physics, apart from the theoretical and practical knowledge of this career, provides distinctive qualities that can give a differential value to the profile of a data scientist. Qualities such as problem posing, the division of the problem into subproblems, or the detection of the key variables in a problem are qualities that can be easily extrapolated to the field of Machine Learning. I am currently part of the Software Department at Deuser, but also I have been part of a research group at the University of Granada in which we apply Machine Learning and Deep Learning techniques in the area of bioinformatics to obtain biomarkers and develop clinical decision systems for various diseases, including COVID-19.Some knowledge or skills acquired related to the sector are:- Data pre-processing and visualization with R (dplyr, ggplot, Hmisc) and Python (NumPy, Matplotlib, Pandas).- Construction of ML models with R and Python in classification problems: Regressions (linear or non-linear), k-NN, SVM, algorithms based on decision trees (RF, XGBoost).- Use of Apache Spark in Big Data problems (MLlib and Sparkpackages).- SQL- Cloudera Impala.- Pig.- Clustering, anomaly detection, association rules.- Deep Learning models: Convolutional neural networks, recurrent networks (keras, TensorFlow, Pytorch).- Bio-inspired algorithms in optimization problems.- Soft Computing.- Cloud Computing.- Docker.- MongoDB NoSQL database.- Probabilistic models.- Process mining.The combination of physics and data analysis allows me to satisfy my interest in knowing and explaining 'the rules of the game'.