and structure win-win commercial deals. Strong hands-on skills in Excel and PowerPoint, including financial modeling and executive presentations. Solid understanding of statistical modeling techniques such as Linear Regression, LogisticRegression, and Bayesian methods. Excellent communication skills-both written and verbal-with the ability to clearly present complex concepts to non-technical stakeholders. Leadership presence with More ❯
and Vertex AI (developing ML services). Expertise: Solid understanding of computer science fundamentals and time-series forecasting. Machine Learning: Strong grasp of ML and deep learning algorithms (e.g. LogisticRegression, Random Forest, XGBoost, BERT, LSTM, NLP, Transfer Learning). More ❯
testing, benchmarking, and other robust model testing. Extensive Experience with at least three of the following statistical, econometric, data science, and predictive modeling approaches: Unsupervised Learning; K-Means; Linear Regression; Time-Series/Forecasting; Stress Testing; LogisticRegression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian Estimators. Strong business context knowledge More ❯
testing, benchmarking, and other robust model testing. Extensive Experience with at least three of the following statistical, econometric, data science, and predictive modeling approaches: Unsupervised Learning; K-Means; Linear Regression; Time-Series/Forecasting; Stress Testing; LogisticRegression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian Estimators. Strong business context knowledge More ❯
testing, benchmarking, and other robust model testing. Extensive Experience with at least three of the following statistical, econometric, data science, and predictive modeling approaches: Unsupervised Learning; K-Means; Linear Regression; Time-Series/Forecasting; Stress Testing; LogisticRegression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian Estimators. Strong business context knowledge More ❯
testing, benchmarking, and other robust model testing. Extensive Experience with at least three of the following statistical, econometric, data science, and predictive modeling approaches: Unsupervised Learning; K-Means; Linear Regression; Time-Series/Forecasting; Stress Testing; LogisticRegression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian Estimators. Strong business context knowledge More ❯
testing, benchmarking, and other robust model testing. Extensive Experience with at least three of the following statistical, econometric, data science, and predictive modeling approaches: Unsupervised Learning; K-Means; Linear Regression; Time-Series/Forecasting; Stress Testing; LogisticRegression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian Estimators. Strong business context knowledge More ❯
testing, benchmarking, and other robust model testing. Extensive Experience with at least three of the following statistical, econometric, data science, and predictive modeling approaches: Unsupervised Learning; K-Means; Linear Regression; Time-Series/Forecasting; Stress Testing; LogisticRegression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian Estimators. Strong business context knowledge More ❯
testing, benchmarking, and other robust model testing. Extensive Experience with at least three of the following statistical, econometric, data science, and predictive modeling approaches: Unsupervised Learning; K-Means; Linear Regression; Time-Series/Forecasting; Stress Testing; LogisticRegression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian Estimators. Strong business context knowledge More ❯
testing, benchmarking, and other robust model testing. Extensive Experience with at least three of the following statistical, econometric, data science, and predictive modeling approaches: Unsupervised Learning; K-Means; Linear Regression; Time-Series/Forecasting; Stress Testing; LogisticRegression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian Estimators. Strong business context knowledge More ❯
testing, benchmarking, and other robust model testing. Extensive Experience with at least three of the following statistical, econometric, data science, and predictive modeling approaches: Unsupervised Learning; K-Means; Linear Regression; Time-Series/Forecasting; Stress Testing; LogisticRegression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian Estimators. Strong business context knowledge More ❯
testing, benchmarking, and other robust model testing. Extensive Experience with at least three of the following statistical, econometric, data science, and predictive modeling approaches: Unsupervised Learning; K-Means; Linear Regression; Time-Series/Forecasting; Stress Testing; LogisticRegression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian Estimators. Strong business context knowledge More ❯
testing, benchmarking, and other robust model testing. Extensive Experience with at least three of the following statistical, econometric, data science, and predictive modeling approaches: Unsupervised Learning; K-Means; Linear Regression; Time-Series/Forecasting; Stress Testing; LogisticRegression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian Estimators. Strong business context knowledge More ❯
testing, benchmarking, and other robust model testing. Extensive Experience with at least three of the following statistical, econometric, data science, and predictive modeling approaches: Unsupervised Learning; K-Means; Linear Regression; Time-Series/Forecasting; Stress Testing; LogisticRegression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian Estimators. Strong business context knowledge More ❯
testing, benchmarking, and other robust model testing. Extensive Experience with at least three of the following statistical, econometric, data science, and predictive modeling approaches: Unsupervised Learning; K-Means; Linear Regression; Time-Series/Forecasting; Stress Testing; LogisticRegression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian Estimators. Strong business context knowledge More ❯
testing, benchmarking, and other robust model testing. Extensive Experience with at least three of the following statistical, econometric, data science, and predictive modeling approaches: Unsupervised Learning; K-Means; Linear Regression; Time-Series/Forecasting; Stress Testing; LogisticRegression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian Estimators. Strong business context knowledge More ❯
testing, benchmarking, and other robust model testing. Extensive Experience with at least three of the following statistical, econometric, data science, and predictive modeling approaches: Unsupervised Learning; K-Means; Linear Regression; Time-Series/Forecasting; Stress Testing; LogisticRegression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian Estimators. Strong business context knowledge More ❯
testing, benchmarking, and other robust model testing. Extensive Experience with at least three of the following statistical, econometric, data science, and predictive modeling approaches: Unsupervised Learning; K-Means; Linear Regression; Time-Series/Forecasting; Stress Testing; LogisticRegression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian Estimators. Strong business context knowledge More ❯
testing, benchmarking, and other robust model testing. Extensive Experience with at least three of the following statistical, econometric, data science, and predictive modeling approaches: Unsupervised Learning; K-Means; Linear Regression; Time-Series/Forecasting; Stress Testing; LogisticRegression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian Estimators. Strong business context knowledge More ❯
testing, benchmarking, and other robust model testing. Extensive Experience with at least three of the following statistical, econometric, data science, and predictive modeling approaches: Unsupervised Learning; K-Means; Linear Regression; Time-Series/Forecasting; Stress Testing; LogisticRegression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian Estimators. Strong business context knowledge More ❯
testing, benchmarking, and other robust model testing. Extensive Experience with at least three of the following statistical, econometric, data science, and predictive modeling approaches: Unsupervised Learning; K-Means; Linear Regression; Time-Series/Forecasting; Stress Testing; LogisticRegression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian Estimators. Strong business context knowledge More ❯
of the data science lifecycle. Proficiency in Python (or R), SQL, and experience with notebooks, Git workflows, and Power BI. Working knowledge of supervised machine learning (e.g., gradient boosting, logisticregression), evaluation metrics, and experiment design. Exposure to MLOps concepts, cloud platforms (e.g., Azure), and GenAI tools is a strong plus. Structured thinking, strong problem-solving, and clear More ❯
Salford, Lancashire, England, United Kingdom Hybrid/Remote Options
Vermelo RPO
a track record of developing analysts. Strong academic background in a numerical discipline (eg BSc Mathematics, Computer Science, Data Science). Proficiency in statistical and machine learning techniques (eg logisticregression, clustering, GBMs) and the application of these in a commercial context. Advanced SQL skills with experience with Python and/or R. Solid understanding of customer segmentation More ❯
of the data science lifecycle. Proficiency in Python (or R), SQL, and experience with notebooks, Git workflows, and Power BI. Working knowledge of supervised machine learning (e.g., gradient boosting, logisticregression), evaluation metrics, and experiment design. Exposure to MLOps concepts, cloud platforms (e.g., Azure), and GenAI tools is a strong plus. Structured thinking, strong problem-solving, and clear More ❯
questions and developing hypotheses, and can collaborate with non-Data Scientists to clarify assumptions and influence decisions. You have extensive experience using various analysis techniques, such as linear and logisticregression, significance testing, and statistical modeling. You have a keen interest in using AI tools to support data exploration and analysis, and already have some experience in doing More ❯
