11 of 11 Permanent Logistic Regression Jobs

with the inherent structure of the data. Use Python (and R) to perform statistical analysis, profiling, reporting, and modeling. Develop and interpret multivariate statistical models, such as linear or logistic regression, survival analysis, and other parametric or non-parametric models that predict business metrics. Build machine learning pipelines to aid with decision-making and streamline critical business processes. … statistical programming experience with Python and SQL Experience working with large datasets, text data, and complex relational data models Strong understanding of various statistical methods, such as linear and logistic regression, time series, design of experiments, etc. Exceptional oral and written communication skills Ability to multitask on different projects and manage deadlines Flexibility to work both collaboratively and More ❯

methodologies to answer business questions, including but not limited to: statistical distribution, A/B testing, experimental design, hypothesis testing. Foundational knowledge of statistical modeling methods and techniques, including logistic regression, multivariate linear regression, variable feature selection Solid practical knowledge and experience with tabular datasets and relational database; able to exam, clean and transform datasets programmatically using More ❯

in healthcare or similar complex domains. Knowledge of statistical concepts and data mining methods such as: Hypothesis testing (or A/B testing), distribution analysis, Bayesian estimation, Linear and Logistic Regression, GLMs, text mining, time series analysis, etc. Knowledge of a variety of traditional machine learning techniques such as: feature engineering methods for large scale numerical and categorical 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; Logistic Regression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian Estimators. Strong business context knowledge More ❯

practices within the organization Bonus Points Passion about online communities, games, and Twitch Experience working with software development and operational event data Experience with machine learning methods such as logistic regression, decision trees, and neural networks Perks Medical, Dental, Vision & Disability Insurance 401(k) Maternity & Parental Leave Flexible PTO Amazon Employee Discount Pursuant to the San Francisco Fair 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, logistic regression), 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 ❯

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, logistic regression), 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 logistic regression, 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 ❯

based on key findings from analysis · Create dashboards to answer key client questions within Power BI · With the assistance of senior analysts creating predictive models utilising methods such as Logistic Regression, CHAID and Clustering techniques · Answering quick questions using data to help guide business decisions · Developing and maintaining strong relationships with stakeholders · Peer review of colleague's work … analysing large volumes of data and a solid understanding of database principles and efficiencies desirable. Ideally in SQL server or Databricks. · Basic statistics: familiarity with averages, deviations, correlation and regression is required · A quality first approach to work, with a strong attention to detail. · Excellent written communication: creating clear, concise and actionable materials for the client. · Confident oral communication More ❯

years of experience in general insurance pricing or similar analytical roles. Strong data manipulation abilities with tools such as SAS, R, or Python. Understanding of predictive modelling approaches (e.g., logistic regression, GBMs). What's on Offer Hybrid & Flexible Working: Smart working options plus a minimum of 35 days' annual leave. Health & Well-being: Dental cover, health assessments More ❯

writing, and presenting of research findings Multinational stakeholder management and engagement, including the management of your own steering groups Conduct significant primary quantitative research (hypothesis testing, multiple linear/logistic regression, factor and cluster analysis, Structural Equation Modelling, etc.) Conduct significant primary qualitative research (in depth interviews, focus groups, thematic and content analysis, etc.) Take insights into action More ❯