City of London, London, United Kingdom Hybrid / WFH Options
Gerrard White Consulting
the risk models and working closely with underwriting and technical modelling teams. ✔️ Personal Lines Pricing experience (Motor, Home or Pet) ✔️ Strong skills in predictive modelling – e.g., GLMs, GBMs, GAMs, Random Forests ✔️ Proficiency in R, Python, PySpark, SAS or SQL ✔️ Experience with Radar and/or Emblem ✔️ A numerate degree (e.g., Maths, Stats, Actuarial, Engineering) ✔️ Strong communication and stakeholder management More ❯
the risk models and working closely with underwriting and technical modelling teams. ✔️ Personal Lines Pricing experience (Motor, Home or Pet) ✔️ Strong skills in predictive modelling – e.g., GLMs, GBMs, GAMs, Random Forests ✔️ Proficiency in R, Python, PySpark, SAS or SQL ✔️ Experience with Radar and/or Emblem ✔️ A numerate degree (e.g., Maths, Stats, Actuarial, Engineering) ✔️ Strong communication and stakeholder management More ❯
applications, and the procurement of 3rd party products. Key Skills & Experience: Expert in data science and machine learning, including a range of techniques such as supervised (e.g. decision trees, random forests), unsupervised (e.g. clustering) , and deep learning. Expert knowledge of exploratory data analysis and statistical analysis of large datasets. Solid experience in machine learning ops and A/B More ❯
of the ML platform. In this role, you'll draw on your in-depth knowledge of the ML ecosystem and understanding of varying approaches - whether it's neural networks, random forests, gradient-boosted trees, or sophisticated ensemble methods - to aid decision-making, choosing the right tool for the problem. Your work will also focus on enhancing research workflows to More ❯
of the ML platform. In this role, you'll draw on your in-depth knowledge of the ML ecosystem and understanding of varying approaches - whether it's neural networks, random forests, gradient-boosted trees, or sophisticated ensemble methods - to aid decision-making, choosing the right tool for the problem. Your work will also focus on enhancing research workflows to More ❯
fields. Postgraduate studies and/or specialized courses are an asset, especially in Data Science, Quantitative Finance, or similar. Knowledge of modeling techniques (logit, GLM, time series, decision trees, random forests, clustering), statistical programming languages (SAS, R, Python, Matlab), and big data tools/platforms (Hadoop, Hive, etc.) is desirable. Solid academic record. Strong computer skills. Knowledge of other More ❯
of mathematical competence. The ability to code or have programming experience, especially in Python. Some experience with theoretical concepts of statistical learning (e.g. hypothesis testing, Bayesian Inference, Regression, SVM, Random Forests, Neural Networks, Natural Language Processing, optimisation). Experience with some coding libraries frequently used in data science. The ability to communicate effectively. Experience composing and following a project More ❯
