2023 Campus Intern: Data Scientist
About our Program:
During the 10-week program, our interns work directly with teams who are changing the way the world shops.
The intern experience includes:
• An impactful individual project
• Direct access to leadership
• Executive-led speaker sessions
• Community outreach events
• Networking and social events
• Compensation and housing assistance provided
• Lead small and participates in large data analytics project teams by serving as a technical lead for analytics projects; working with project teams and business partners to determine project goals; developing contingency plans for data analysis; determining modeling based on business needs;
directing the analysis of data; gathering data and developing reports as needed; utilizing business knowledge to ensure data supports project goals; analyzing data based on identified variables; reviewing data results to ensure accuracy; and communicating results and insights to the project team and business partners.
• Present data insights and recommendations to key stakeholders by developing insights based on data analysis; applying analytical results to project goals; identifying trends and key insights; translating results into business actions; and presenting insights and recommendations to key stakeholders.
• Participates in the continuous improvement of data science and analytics by developing replicable solutions (for example, codified data products, project documentation, process flowcharts) to ensure solutions are leveraged for future projects; building and maintaining a library of reusable
algorithms for future use; ensuring developed code is documented; and coaching and mentoring analysts across the division and project teams.
• Develop analytical models to drive analytics insights by gathering data from internal and external sources; evaluating data usability based on project goals; synthesizing data into large datasets to support project goals; developing statistical models and computational algorithms to analyze data;
utilizing the analytics project lifecycle process to drive predictive modeling; coding, testing, and maintaining analytical software tools; identifying trends, patterns and discrepancies in data; training statistical models for replication for future projects; and presenting data insights and
recommendations to key stakeholders.