1 to 25 of 62 Permanent Logistic Regression Jobs

as is a general willingness to constantly learn and improve one's technical skill set. This role also requires the ability to conduct statistical modeling (such as linear and logistic regression) in order to inform divisional strategy, and work with non-technical stakeholders to understand key questions and problems and use a variety of approaches to address them More ❯

Management frameworks and associated regulations (e.g. SS1/23, Consumer Duty) Familiarity with data science processes (e.g. pipelines) and associated technologies Exposure to range of risk modelling techniques (e.g. logistic and time series regressions and Machine Learning methods, such as gradience boosting, random forests, etc.) Red Hot Rewards Generous holidays - 38.5 days annual leave (including bank holidays and prorated More ❯

Management frameworks and associated regulations (e.g. SS1/23, Consumer Duty) Familiarity with data science processes (e.g. pipelines) and associated technologies Exposure to range of risk modelling techniques (e.g. logistic and time series regressions and Machine Learning methods, such as gradience boosting, random forests, etc.) Red Hot Rewards Generous holidays - 38.5 days annual leave (including bank holidays and prorated More ❯

Management frameworks and associated regulations (e.g. SS1/23, Consumer Duty) Familiarity with data science processes (e.g. pipelines) and associated technologies Exposure to range of risk modelling techniques (e.g. logistic and time series regressions and Machine Learning methods, such as gradience boosting, random forests, etc.) Red Hot Rewards Generous holidays - 38.5 days annual leave (including bank holidays and prorated More ❯

Management frameworks and associated regulations (e.g. SS1/23, Consumer Duty) Familiarity with data science processes (e.g. pipelines) and associated technologies Exposure to range of risk modelling techniques (e.g. logistic and time series regressions and Machine Learning methods, such as gradience boosting, random forests, etc.) Red Hot Rewards Generous holidays - 38.5 days annual leave (including bank holidays and prorated More ❯

Management frameworks and associated regulations (e.g. SS1/23, Consumer Duty) Familiarity with data science processes (e.g. pipelines) and associated technologies Exposure to range of risk modelling techniques (e.g. logistic and time series regressions and Machine Learning methods, such as gradience boosting, random forests, etc.) Red Hot Rewards Generous holidays - 38.5 days annual leave (including bank holidays and prorated More ❯

manage deliverables What you'll 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. Logistic Regression, Random Forest, XGBoost, etc), state-of-the-art research area (e.g. NLP, Transfer Learning etc) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc) Solid knowledge of More ❯

accept feedback Manage multiple data science projects and deliverables Qualifications Strong understanding of computer science fundamentals: data structures, algorithms, data modeling, software architecture Experience with classical ML algorithms (e.g., Logistic Regression, Random Forest, XGBoost), research areas (e.g., NLP, Transfer Learning), and Deep Learning (e.g., BERT, LSTM) Proficiency in SQL and Python (Jupyter, Pandas, Scikit-learn, Matplotlib) Knowledge of More ❯

science projects and manage deliverables 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., Logistic Regression, Random Forest, XGBoost, etc.), state-of-the-art research areas (e.g., NLP, Transfer Learning, etc.), and modern Deep Learning algorithms (e.g., BERT, LSTM, etc.) Solid knowledge of More ❯

general insurance products and/or pricing teams, including knowledge of current trends and issues in motor or home pricing Experience with some of the following predictive modelling techniques; Logistic Regression, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Neural Nets and Clustering. Knowledge of the technical differences between different packages for some of these model types More ❯

general insurance products and/or pricing teams, including knowledge of current trends and issues in motor or home pricing Experience with some of the following predictive modelling techniques; Logistic Regression, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Neural Nets and Clustering. Knowledge of the technical differences between different packages for some of these model types More ❯

manage deliverables What you'll 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. Logistic Regression, Random Forest, XGBoost, etc), state-of-the-art research area (e.g. NLP, Transfer Learning etc) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc) Solid knowledge of More ❯

Proficiency: Strong skills with libraries like pandas, NumPy, scikit-learn, PyTorch, and Hugging Face Transformers. Ability to write clean, modular, and testable code. Traditional Machine Learning Models: Experience with regression (linear, ridge), classification (logistic regression, decision trees, random forests), clustering (k-means, DBSCAN), and time-series forecasting (ARIMA, Prophet). Model evaluation, tuning, and deployment. Business Requirement More ❯

accept feedback Manage multiple data science projects and deliverables Qualifications Strong understanding of computer science fundamentals: data structures, algorithms, data modeling, software architecture Experience with classical ML algorithms (e.g., Logistic Regression, Random Forest, XGBoost), research areas (e.g., NLP, Transfer Learning), and Deep Learning (e.g., BERT, LSTM) Proficiency in SQL and Python (Jupyter, Pandas, Scikit-learn, Matplotlib) Knowledge of More ❯

Financial Engineering, Technology or Engineering Knowledge of probability theory, inferential statistics, machine learning, Bayesian statistics, linear algebra, and numerical methods Experience with statistical and machine learning models, such as regression-based models (e.g., logistic regression, linear regression, negative binomial regression), tree-based models (e.g., random forests), support vector machines, PCA, clustering models, matrix factorization, deep More ❯

true to our Customers, Owners and Colleagues alike. Essential Experience: Extensive experience designing, developing and deploying machine learning and AI solutions in production environments Statistical modelling, machine learning (e.g. logistic regression, random forest, XGBoost, and modern deep learning techniques (e.g. transformers, transfer learning, reinforcement learning) Proven ability to lead technical direction across projects or domains Expertise in model More ❯

Computer Science, Financial Engineering, Technology or EngineeringKnowledge of probability theory, inferential statistics, machine learning, Bayesian statistics, linear algebra, and numerical methodsExperience with statistical and machine learning models, such as regression-based models (e.g., logistic regression, linear regression, negative binomial regression), tree-based models (e.g., random forests), support vector machines, PCA, clustering models, matrix factorization, deep More ❯

and above). Strong numerical background with a knowledge of key statistical principals e.g. Bayesian and frequentist statistics, probability distributions. Fundamental understanding of ML algorithms e.g. linear and logistic regression, random forest, neural networks, time series models. Experience with multiple programming languages, with a preference for SQL and Python. Familiarity with large language models and prompt engineering would More ❯

performing statistical analyses leading to the understanding of the structure of data sets. Proven and demonstrable experience with at least three of the following machine learning algorithms: neural networks, logistic regression, non-linear regression, random forests, decision trees, support vector machines, linear/non-linear optimization. Experience working with Java and Python, and strong understanding of data More ❯

performing statistical analyses leading to the understanding of the structure of data sets. Proven and demonstrable experience with at least three of the following machine learning algorithms: neural networks, logistic regression, non-linear regression, random forests, decision trees, support vector machines, linear/non-linear optimization. Experience working with Java and Python, and strong understanding of data More ❯

performing statistical analyses leading to the understanding of the structure of data sets. Proven and demonstrable experience with at least three of the following machine learning algorithms: neural networks, logistic regression, non-linear regression, random forests, decision trees, support vector machines, linear/non-linear optimization. Experience working with Java and Python, and strong understanding of data More ❯

more. If you also bring front-end skills in React , that’s a big plus! Key Responsibilities Design and implement end-to-end machine learning models using Python for regression, classification, clustering, NLP, and deep learning tasks. Build and deploy AI applications leveraging: Retrieval Augmented Generation (RAG) LangChain and Prompt Engineering LLMs (OpenAI, Huggingface Transformers) Vector Databases (FAISS, Pinecone … using React.js for interactive AI applications. Required Skills & Experience 5+ years of hands-on AI/ML development using Python. Strong knowledge of: ML algorithms: XGBoost, SVM, Random Forests, Logistic Regression Deep learning models: CNNs, RNNs, Transformers (BERT, GPT) Unsupervised learning: K-Means, PCA Experience with: RAG architecture, LangChain, and advanced prompt engineering Vector search techniques (BM25, Dense More ❯

of experimentation methodologies (A/B testing, multivariate testing, etc.) and their application in product decision-making. In-depth knowledge of statistical and machine learning models: gradient boosted trees, logistic regression, neural networks, survival analysis, etc. Experience with end-to-end model development and maintenance of ML models used for business-critical decisions. Solid understanding of key product … you built or launched AI or ML-powered products in a professional setting? * Select... Which statistical or machine learning models have you worked with extensively? (Select all that apply) * Logistic Regression Survival Analysis Other Have you worked in or supported a product related to search or discovery (e.g., eCommerce search engines, recommender systems)? * Select... #J-18808-Ljbffr More ❯

vision and long-term goals of the company. Key Skills and Experience: Previous experience within Personal Lines Pricing is advantageous Experience with some of the following predictive modelling techniques; Logistic Regression, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Neural Nets and Clustering Experience in statistical and data science programming languages (e.g. R, Python, PySpark, SAS, SQL More ❯

Mathematics, Statistics, Data Science, Economics, or Physics 1 to 2 years of experience in a Financial data driven environment, or strong academic project experience Familiarity with modelling techniques like logistic regression or basic machine learning A keen interest in data science and its applications in finance or risk Strong attention to detail and a problem solving mindset A More ❯