Job Information
Total Quality Logistics, LLC Data Scientist in Cincinnati, Ohio
About the role: As Data Scientist for Total Quality Logistics, LLC, you will be responsible for Data Science, Machine Learning, Statistics, and Applied Mathematics across the organization involving prototyping, optimizing, validating, analyzing, tuning, simulating, and updating machine learning and statistical models from ideation, model development, data visualization, to final delivery.
What you'll be doing:
Developing advanced analytic and machine learning approaches to solve problems, while streamlining and optimizing the framework for building production-level models.
Providing guidance to teammates with varying levels of experience in intricate models and statistics, including regression, diagnostic stats, PCA, and Markov Chains to help scale and deliver advanced analytics projects translating complex predictive, prescriptive, and inferential analysis and methodology into operationally practical insights.
Working with Python and R-based machine learning frameworks, and data and computation management, in an on-premise setting with the ability to migrate to a cloud setting (AWS, MS Azure).
Assessing Python-based ML frameworks, particularly those requiring memory-intensive processing for datasets such as time series, forecasting, fraud detection, and document image classification.
What you need:
PhD in Data Science, Mathematics, Statistics, or related field. 1 year of experience in Data Science, Machine Learning, Statistics, or Applied Mathematics. 1 year of experience in prototyping, optimizing, validating, analyzing, tuning, simulating, and updating machine learning and statistical models. 1 year of experience teaching complex models and statistics to teammates with different levels of experience, from regression and diagnostic statistics to advanced imputation like PCA or Markov Chains for complex distributions. 1 year of experience building & testing Python-based machine learning frameworks primarily using on-premise datacenter resources in memory across many disparate data sets specifically including time series, forecasting, fraud detection, and document image classification. 1 year of experience in application of statistical modeling languages (e.g. R, Python, SAS, etc). 1 year of experience proving out new machine learning Python or R libraries or methods and creating reusable experimentation frameworks for use across multiple teams. 1 year of experience with cloud compute methods in MS Azure, AWS, GCP, or related frameworks. 1 year of experience with SQL; Tableau, PowerBI, or Matlab for visualization; and MS Office.
Alternatively, Master's degree in Data Science, Mathematics, Statistics, or related field. 4 year of experience in Data Science, Machine Learning, Statistics, or Applied Mathematics. 4 year of experience in prototyping, optimizing, validating, analyzing, tuning, simulating, and updating machine learning and statistical models. 4 year of experience teaching complex models and statistics to teammates with different levels of experience, from regression and diagnostic statistics to advanced imputation like PCA or Markov Chains for complex distributions. 4 year of experience building & testing Python-based machine learning frameworks primarily using on-premise datacenter resources in memory across many disparate data sets specifically including time series, forecasting, fraud detection, and document image classification. 4 year of experience in application of statistical modeling languages (e.g. R, Python, SAS, etc). 4 year of experience proving out new machine learning Python or R libraries or methods and creating reusable experimentation frameworks for use across multiple teams. 4 year of experience with cloud compute
