14 of 14 Logistic Regression Jobs

such as Basel or IFRS9 (Preferred) Education: Degree level education in a quantitative field (Required) Hard Skills: Statistics: Knowledge of statistical techniques such as logistic regression, linear regression, time series analysis, and survival analysis (Required) Programming: Proficiency in statistical programming languages such as SAS or Python (Required ...

postgraduate preferred) in a quantitative discipline such as Computer Science, Statistics, Mathematics, Economics, or similar Working knowledge of statistical and machine learning methods (e.g. logistic regression, gradient boosting, random forests, clustering, NLP Proficient in Python and/or R, with strong experience in data quality, model development ...

with good understanding of best practices in software engineering and data engineering In-depth knowledge of statistical and machine learning models: gradient boosted trees, logistic regression, neural networks, survival analysis, etc Solid knowledge of statistics: hypothesis testing, confidence intervals, bootstrap Experience of end-to-end model development ...

with good understanding of best practices in software engineering and data engineering In-depth knowledge of statistical and machine learning models: gradient boosted trees, logistic regression, neural networks, survival analysis, etc Solid knowledge of statistics: hypothesis testing, confidence intervals, bootstrap Experience of end-to-end model development ...

with good understanding of best practices in software engineering and data engineering In-depth knowledge of statistical and machine learning models: gradient boosted trees, logistic regression, neural networks, survival analysis, etc Solid knowledge of statistics: hypothesis testing, confidence intervals, bootstrap Experience of end-to-end model development ...

with good understanding of best practices in software engineering and data engineering In-depth knowledge of statistical and machine learning models: gradient boosted trees, logistic regression, neural networks, survival analysis, etc Solid knowledge of statistics: hypothesis testing, confidence intervals, bootstrap Experience of end-to-end model development ...

with good understanding of best practices in software engineering and data engineering In-depth knowledge of statistical and machine learning models: gradient boosted trees, logistic regression, neural networks, survival analysis, etc Solid knowledge of statistics: hypothesis testing, confidence intervals, bootstrap Experience of end-to-end model development ...

with good understanding of best practices in software engineering and data engineering In-depth knowledge of statistical and machine learning models: gradient boosted trees, logistic regression, neural networks, survival analysis, etc Solid knowledge of statistics: hypothesis testing, confidence intervals, bootstrap Experience of end-to-end model development ...

with good understanding of best practices in software engineering and data engineering In-depth knowledge of statistical and machine learning models: gradient boosted trees, logistic regression, neural networks, survival analysis, etc Solid knowledge of statistics: hypothesis testing, confidence intervals, bootstrap Experience of end-to-end model development ...

with good understanding of best practices in software engineering and data engineering In-depth knowledge of statistical and machine learning models: gradient boosted trees, logistic regression, neural networks, survival analysis, etc Solid knowledge of statistics: hypothesis testing, confidence intervals, bootstrap Experience of end-to-end model development ...

with good understanding of best practices in software engineering and data engineering In-depth knowledge of statistical and machine learning models: gradient boosted trees, logistic regression, neural networks, survival analysis, etc Solid knowledge of statistics: hypothesis testing, confidence intervals, bootstrap Experience of end-to-end model development ...

multivariate methods— selecting the appropriate technique based on the business question and clearly communicating trade-offs. Build, evaluate, and iterate on statistical models (e.g., regression, clustering, classification), correctly interpreting outputs and translating findings into actionable recommendations. Develop clear, audience-appropriate data visualizations and narratives that communicate insights to both … varied analytical techniques. Advanced proficiency in SQL, Python, or R for data extraction, transformation, and visualisation at scale. Proficient understanding of statistical concepts including regression, ANOVA, probability, and frequentist vs. Bayesian approaches, with the ability to distinguish statistically significant results from exploratory analysis. Experience applying a range of modelling ...

power and sample-size calculations within the context of study design and development Ability to use and re-fit models typically used in healthcare (logistic regression, decision trees, GLMMs, etc.), including implementing novel methods from publications. Own the data analysis for ongoing research projects Write and maintain code ...

consultants on analytic client engagements involving descriptive, predictive, and prescriptive analysis through the consumer lending portfolio lifecycle, leveraging a variety of techniques (e.g., segmentation, logistic regression, etc). You will deliver analytic insights and recommendations in succinct and compelling presentations for internal and external customers. You will design ...