25 of 25 Permanent Logistic Regression Jobs

basic/routine predictive models and algorithms with some complexity Utilize traditional and machine learning techniques and tools to build a variety of models, including but not limited to logistic regression, random forest, XGBoost, neural networks, NLP, k-means clustering, ARIMA, and prophet forecasting. Analyze and interpret results with some complexity Exercise limited judgment and discretion within defined … analysis Ability to use data and cloud environments such as Azure, Databricks, AWS, or Hadoop Experience with machine learning techniques and tools to build a variety of models, including logistic regression, XGBoost, neural networks, NLP, and clustering Technical writing skills Strong communication and data storytelling presentation skills of technical material Ability to collaborate and build relationships Desired: Bachelor More ❯

basic/routine predictive models and algorithms with some complexity Utilize traditional and machine learning techniques and tools to build a variety of models, including but not limited to logistic regression, random forest, XGBoost, neural networks, NLP, k-means clustering, ARIMA, and prophet forecasting. Analyze and interpret results with some complexity Exercise limited judgment and discretion within defined … analysis Ability to use data and cloud environments such as Azure, Databricks, AWS, or Hadoop Experience with machine learning techniques and tools to build a variety of models, including logistic regression, XGBoost, neural networks, NLP, and clustering Technical writing skills Strong communication and data storytelling presentation skills of technical material Ability to collaborate and build relationships Desired: Bachelor More ❯

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 ❯

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 ❯

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 ❯

business insights and support strategic decision-making. Design and apply advanced predictive and machine learning models; including clustering (K-means, hierarchical), classification (KNN, Naive Bayes, CART), time series forecasting, logistic regression, and econometric models to optimize pricing strategies, assess price elasticity, segment customers, and enhance revenue across channels. Leverage generative AI and large language models (LLMs) to develop More ❯

business insights and support strategic decision-making. Design and apply advanced predictive and machine learning models; including clustering (K-means, hierarchical), classification (KNN, Naive Bayes, CART), time series forecasting, logistic regression, and econometric models to optimize pricing strategies, assess price elasticity, segment customers, and enhance revenue across channels. Leverage generative AI and large language models (LLMs) to develop More ❯

of work experience with a PhD •With a track record of successfully delivering complex software systems or services as Senior Data Scientist •Relevant coursework in modeling techniques such as logistic regression, Naïve Bayes, SVM, decision trees, or neural networks. •Ability to program in one or more scripting languages such as Perl or Python and one or more programming 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 ❯

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 ❯

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 ❯

e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience - 4+ years of data scientist experience - Experience with statistical models e.g. multinomial logistic regression PREFERRED QUALIFICATIONS - 2+ years of data visualization using AWS QuickSight, Tableau, R Shiny, etc. experience - Experience managing data pipelines Amazon is an equal opportunity employer and does More ❯

and who have the will to change it. Technical Skills: Proficiency in the modern machine learning techniques, such as Model Evaluation and Validation, Deep Learning and Time Series Analysis, Logistic Regression, Naive Bayes, Random Forest Models Self Starter: Confidence to prioritize work and delivery demonstrable results on a tight cadence. Domain Knowledge: Demonstrated experience or understanding of the More ❯

teams to integrate ML models into production systems, enabling autonomous real-time decision-making at scale Evaluate and implement the optimal statistical and modeling approaches-from traditional techniques like logistic regression and segmentation to modern deep learning architectures-based on problem requirements Champion a culture of experimentation, innovation, and continuous learning within the data science organization What Sets … and iteration Proficiency with modern ML frameworks, cloud platforms, and MLOps tools for building scalable AI systems Proven track record selecting and applying advanced statistical and ML techniques-classification, regression, clustering, ensemble methods, neural networks-to drive business outcomes Ability to balance technical sophistication with pragmatic delivery, knowing when to leverage state-of-the-art methods versus proven approaches More ❯

Learning, Data Mining, Statistics, or related technical field with preferred 3+ years of relevant experience Deep knowledge and experience with at least one ML model: LLMs, GNN, Deep Learning, Logistic Regression, Gradient Boosting trees, etc. Working knowledge in one or more of the following: generative AI, data mining, information retrieval, advanced statistics or natural language processing, computer vision More ❯

experience with real data for customer insights, business and market analysis will be advantageous. Experience with text analytics, data mining and social media analytics. Statistical knowledge in standard techniques: Logistic Regression, Classification models, Cluster Analysis, Neural Networks, Random Forests, Ensembles, etc. More ❯

experience with real data for customer insights, business and market analysis will be advantageous. Experience with text analytics, data mining and social media analytics. Statistical knowledge in standard techniques: Logistic Regression, Classification models, Cluster Analysis, Neural Networks, Random Forests, Ensembles, etc. 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 ❯

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 ❯

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 ❯