Data Engineer and Scientist with proven analytical, modeling, leadership, and big data engineering skills, seeking to enable insight and growth through data driven decisions.
Stian Ulriksen has half a decade of experience working with data science and data engineering in a variety of industries. Stian has demonstrated advanced skills in developing machine learning algorithms, data models, econometric models, intuitive visualizations and reporting dashboards.
Stian specializes in communicating data and technical terminology in an easy to understand manner for clients of varying backgrounds.
One of the most common tools to make inferences and predictions.
A foundational concept for making classifications.
Modeling a database correctly following best practices is key for performance.
Utilizes iterative learning process common in classification, forecasting, and predicting.
Solid ETL/ELT workflows are the foundation to a strong data warehouse.
Data can be difficult to understand if it is not presented correctly.
Developed, tested, and maintained data pipelines and related architectures within the Azure platform. Worked as a team lead/mentor to help facilitate the usage of more sophisticated analytics/data science throughout the organization.
Successfully managed data science teams and project life cycles from inception to production. Designed and deployed linear and non-linear machine learning models. Built repeatable, functional production-ready data science code for clients.
Designed and Implemented Machine Learning Models in Python (Sklearn, Pandas, Numpy). Deployed intuitive and easy to understand visualizations through Power BI and SiSense. Devised complex pricing models employing Econometric Modeling to maximize revenue.