bring Solid understanding of computer science fundamentals, including data structures, algorithms, data modelling and software architecture Solid understanding of classical Machine Learning algorithms (e.g. LogisticRegression, Random Forest, XGBoost, etc), state-of-the-art research area (e.g. NLP, Transfer Learning etc) and modern Deep Learning algorithms (e.g. BERT More ❯
Qualifications Solid understanding of computer science fundamentals, including data structures, algorithms, data modelling, and software architecture Solid understanding of classical Machine Learning algorithms (e.g., LogisticRegression, Random Forest, XGBoost, etc.), state-of-the-art research areas (e.g., NLP, Transfer Learning, etc.), and modern Deep Learning algorithms (e.g., BERT More ❯
Qualifications Solid understanding of computer science fundamentals, including data structures, algorithms, data modelling, and software architecture Solid understanding of classical Machine Learning algorithms (e.g., LogisticRegression, Random Forest, XGBoost, etc.), state-of-the-art research areas (e.g., NLP, Transfer Learning, etc.), and modern Deep Learning algorithms (e.g., BERT More ❯
prices to meet budget requirements. Key Skills and Experience: Previous experience within general insurance pricing Experience with some of the following predictive modelling techniques; LogisticRegression, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Neural Nets and Clustering Experience in statistical and data science programming languages (e.g. More ❯
probability theory, or optimization. Experience and training in finance and operations domains. Deep experience with ML approaches: deep learning, generative AI, large language models, logisticregression, gradient descent. Experience wrangling complex and diverse data to solve real-world problems. What's it like to work here: You will More ❯
model building using industry standard time Series Forecasting techniques like ARIMA, ARIMAX, Holt Winter and formulate ensemble model. Proficiency in both Supervised (Linear/LogisticRegression) and Unsupervised algorithms (k means clustering, Principal Component Analysis, Market Basket analysis). Experience in solving optimization problems like inventory and network More ❯
and Docker is a positive. An understanding of machine learning processes and their applications to investments. Ideally, they will be familiar with various algorithms (logisticregression, neural networks) and an understanding of how to implement these in Python. A passion for bringing together investment ideas in an organized More ❯
Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience. 5+ years of data scientist experience. Experience with statistical models e.g. multinomial logistic regression. PREFERRED QUALIFICATIONS Experience working with data engineers and business intelligence engineers collaboratively. Experience managing data pipelines. Experience as a leader and mentor on More ❯
Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience. 5+ years of data scientist experience. Experience with statistical models e.g. multinomial logistic regression. PREFERRED QUALIFICATIONS Experience working with data engineers and business intelligence engineers collaboratively. Experience managing data pipelines. Experience as a leader and mentor on More ❯
Developing and deploying ML use-cases. Utilizing AIML development tools such as PyTorch, Jupyter Notebooks, XGBoost, TensorFlow, etc. Building AIML models including Linear/LogisticRegression, KNN, Decision Trees, Anomaly Detection, Large Language Models (LLMs), Generative Models (PALM, GPT-3/4), Entity Extraction, among others. #J More ❯
insurance pricing or a related analytical background. Proficient in using programming languages (e.g., SAS) to manipulate data. Experience with predictive modelling techniques such as LogisticRegression, Log-Gamma GLMs, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Support Vector Machines, and Neural Nets. Skilled in programming languages More ❯
Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience. 5+ years of data scientist experience. Experience with statistical models e.g. multinomial logistic regression. Experience developing neural network models. PREFERRED QUALIFICATIONS 2+ years of data visualization using AWS QuickSight, Tableau, R Shiny, etc. experience. Experience managing data More ❯
Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience. 5+ years of data scientist experience. Experience with statistical models e.g. multinomial logistic regression. Experience developing neural network models. PREFERRED QUALIFICATIONS 2+ years of data visualization using AWS QuickSight, Tableau, R Shiny, etc. experience. Experience managing data More ❯
the main concepts of Data Science/Machine Learning would be useful. We use a range of predictive analytics and machine learning methodologies, including logisticregression and cluster analysis, plus some predictive time series analysis. More ❯
the main concepts of Data Science/Machine Learning would be useful. We use a range of predictive analytics and machine learning methodologies, including logisticregression and cluster analysis, plus some predictive time series analysis. More ❯
