Skill
Decision Trees
Data Science, Analytics and AI/ML
A decision tree is a flowchart-like structure used in both decision analysis and machine learning, where internal nodes represent tests on attributes, branches represent outcomes, and leaves represent final decisions or predictions. In business, they help map out choices and their consequences, such as in risk analysis or project planning. In data science, they form the basis of predictive models and are often combined into ensembles like random forests to improve accuracy.
Open roles requiring Decision Trees (2)
Data Scientist
Auto-Owners Insurance
Lansing, MI
This role involves building statistical and machine learning models to solve insurance business problems and improve decision-making across the company. It suits someone with a quantitative background and 2-4 years of professional modeling experience who wants to work collaboratively on high-impact analytics in a hybrid environment.
Listed on Auto-Owners Insurance’s careers site · Apply there ↗
Experienced Data Scientist
Auto-Owners Insurance
Full-time · Lansing, MI
This role leads the development and deployment of statistical and machine learning models to solve complex insurance business problems, requiring deep expertise in data science techniques and large-scale data handling. It suits experienced data scientists who want to mentor others and influence decision-making across a large organization.
Listed on Auto-Owners Insurance’s careers site · Apply there ↗
Roles that use Decision Trees
Related skills
Curated neighbors in the taxonomy, whether or not employers ask for them together.