semi-structured and unstructured data. Ability to use dimension reduction techniques (PCA, encoders etc.) Excellent familiarity with elastic net logistic regression, random forest and XGBoost ensembles to work on supervised problems with structured, tabular data. We currently use Scikit-learn, and we're open to suggestions for additional libraries. Classification more »
building using R and/or Python languages (generalized linear models, logistics/linear Regression, Tree based and Boosting models, Decision trees, Random Forest, XGBoost etc.- Basic understanding of Neural network and deep learning models). Possess experience with Machine learning/Pattern recognition areas. Should possess basic Data Engineering more »
Brighton, England, United Kingdom Hybrid / WFH Options
15gifts
data science tech stack Python Docker & Kubernetes AWS Cloud Deep learning frameworks - Pytorch and Tensorflow HuggingFace ecosystem [optional] Other machine learning frameworks - scikit-learn, XGboost, CatBoost etc Ability to understand and develop state-of-the-art implementations Familiarity with state-of-the-art deep learning (e.g. transformers) and reinforcement learning more »
or Azure) Strong proficiency in NumPy for numerical computing and data manipulation tasks. Proven knowledge of Machine Learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, etc.) as well as state-of-the-art research area (e.g. NLP, Transfer Learning etc.) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc.) Bachelor more »
london, south east england, United Kingdom Hybrid / WFH Options
Sanderson
or Azure) Strong proficiency in NumPy for numerical computing and data manipulation tasks. Proven knowledge of Machine Learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, etc.) as well as state-of-the-art research area (e.g. NLP, Transfer Learning etc.) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc.) Bachelor more »
hands-on experience in machine learning engineering. Proven expertise in regression modelling and time series modelling. Numpy/Pandas/Keras/Tensorslow/XGBoost/Scikit-learn Experienced in GCP preferably Extensive background in deploying and productionizing machine learning models. Strong programming skills in languages such as Python, R more »
hands-on experience in machine learning engineering. Proven expertise in regression modelling and time series modelling. Numpy/Pandas/Keras/Tensorslow/XGBoost/Scikit-learn Experienced in GCP preferably Extensive background in deploying and productionizing machine learning models. Strong programming skills in languages such as Python, R more »
one or more additional programming languages (Java, JavaScript, C++, Go, C#) is highly beneficial. Experience of machine learning frameworks and toolkits such as sklearn, XGboost, TensorFlow Good cloud skills AWS & Azure Familiarity with Unix and scripting languages. Proficient with Docker working in any container orchestrator such as k8s, OpenShift, Docker more »
