EOSYS · 8 hours ago
Associate Commercial Data Science Intern
EOSYS is a management consulting firm that provides data-driven insights to help organizations grow revenue. The Associate Commercial Data Science Intern will work with various teams to apply statistical methods and machine learning approaches while communicating findings to clients and stakeholders.
Industrial AutomationIndustrial DesignIndustrial EngineeringInformation TechnologyManufacturing
Responsibilities
Support teams engaged with big data environments
Apply and interpret advanced statistical & machine learning approaches
Advise teams with data preparation and model-building tasks
Use and develop communication skills to communicate project findings, conclusions and recommendations with your peers and clients
Participate in client meetings to review and present analytical approaches and interpret results
Support the use of automated Alexander Group internal tools for project-related analytics
Help consolidate modeling approaches for Alexander Group service offerings
Support Commercial Analytics practice development (e.g., analytical trainings, new offering development, etc.)
Qualification
Required
Pursuit of a Bachelor's Degree with solid academic achievement
Entry-level knowledge of supervised & unsupervised ML algorithms for regression/classification
Entry-level proficiency with open-source data science languages like R or Python
Entry-level experience with Data Visualization tools like PowerBI or Tableau
Ability to manage multiple priorities/projects
Problem solving skills for business problems
Strong interpersonal and team working skills
Good communication skills (written and verbal)
Ability to learn quickly and resourcefully
High degree of motivation, flexibility and creativity
Positive attitude and strong willingness to learn from mentors and peers
Ability to travel (sometimes on short notice)
Preferred
Coursework in Mathematics, Economics, Data Science, Statistics, or Computer Science strongly preferred
Moderate Excel and PowerPoint skills are preferred