manage deliverables What we offer Solid understanding of computer science fundamentals, data structures, algorithms, data modelling and software architecture Solid understanding of classical Machine Learning algorithms (e.g. Logistic Regression, RandomForest, XGBoost, etc.), state-of-the-art areas (e.g. NLP, Transfer Learning) and modern Deep Learning algorithms (e.g. BERT, LSTM) Solid knowledge of SQL and Python's ecosystem More ❯
Python, Tensorflow (essential) Database experience, preferably SQL (essential) Expertise in cutting-edge AI methodologies, including Generative AI and Reinforcement Learning Machine learning - Supervised/unsupervised learning, regression, decision trees, random forests, boosting, clustering (essential) The rewards There's one thing people can't stop talking about when it comes to : the perks. Here's a taster: Sky Q, for More ❯
processing of and cleaning of data, merging/joining disparate data sources, feature engineering, performing analyses and communicating results). Produce models using a variety of algorithms (GLM, GBM, RandomForest, Neural Networks etc.) and assess the relative strength of each model Identify which factors are relevant and predictive and should be included in the model build Document More ❯
and understanding of statistical methods such as time to event analysis, machine learning, meta-analysis, mixed effect modeling, longitudinal modeling, Bayesian methods, variable selection methods (e.g., lasso, elastic net, randomforest), design of clinical trials. Strong programming skills in R and Python. Demonstrated knowledge of data visualization, exploratory analysis, and predictive modeling. Excellent interpersonal and communication skills (verbal More ❯
