AI Data Science & Business Analytics Intern
- Compensation
- Not disclosed
- Location
- Durham, NC
- Work setup
- Not specified
- Seniority
- Intern
- Employment
- Internship
- Experience
- Not specified
- Category
- Data science
- Posted
Skills
Must have
- Must be able to work on-site at our Durham, NC headquarters at least one day per week; two days preferred.
- Currently pursuing a bachelor’s or master’s degree in Data Science, Business Analytics, AI, Statistics, Computer Science, Mathematics, Economics, Business Administration, or a related field.
- Strong experience building, validating, and refining predictive models (e.g., regression, classification, clustering, time-series forecasting) through work, internships, research, or substantial proje
- Strong understanding of AI/LLMs, including how they work, their limitations, and where they can be applied to analytics.
- Experience working with Salesforce or other CRM data, including opportunities, stages, accounts, and pipeline history.
- Proficiency in Python or R, including common data science libraries such as pandas and scikit-learn.
- Strong SQL skills and comfort working with large, messy, real-world datasets.
- Working knowledge of Excel and Microsoft Power Platform tools (e.g., Power Query, Power Automate) to analyze data and automate workflows.
- Experience with data visualization tools such as Power BI or Tableau.
- Excellent business judgment, strong analytical thinking, and attention to detail, with the ability to explain technical results clearly to non-technical audiences.
- Curiosity, initiative, and a willingness to experiment with new technologies, take ownership of tasks, and learn through hands-on problem-solving.
Nice to have
- Pursuing a master’s degree in a quantitative or analytics-focused program.
Full description
Spring 2027 Internship: This position is located at our HQ in Durham, NC (Research Triangle Park). Expected program dates are January 19 – April 23, 2027. Anticipated working hours will be 15-30 per week. Final hours and schedule will be determined between the intern and their manager.
Note: Toshiba Global Commerce Solutions will not be providing visa sponsorship for this position now or in the future. Therefore, in order to be considered for this position, you must have the ability to work without a need for current or future visa sponsorship.
Introduction:
Our Solutions Group analyzes the financial performance of our software product portfolio and the drivers of our P&L. We’re looking for an analytically strong, business-minded student to make our forecasting more predictive and more robust. You’ll work deeply with Salesforce pipeline data and build models that segment and score opportunities, forecast revenue, and surface the leading indicators behind forecast outcomes. You’ll also turn that work into insights that shape business planning.
What You’ll Work On:
- Predictive forecasting: Develop and enhance predictive models that improve revenue forecasting accuracy, strengthen pipeline visibility, and give forward-looking insight into revenue performance.
- Pipeline analytics: Analyze Salesforce opportunity and pipeline data to find patterns in deal progression, stage conversion, win rates, deal velocity, and revenue realization.
- Modeling and segmentation: Apply statistical analysis, clustering, regression, classification, and other techniques to segment opportunities, estimate their likelihood and revenue potential, and identify the key drivers of forecast outcomes.
- Risk and leading indicators: Identify leading indicators, pipeline risks, and emerging trends to explain forecast variability and improve the reliability of revenue projections.
- Data modeling and pipeline building: Explore and document Salesforce data structures, and build clean, well-modeled datasets and data pipelines to support repeatable analysis and modeling.
- Portfolio and P&L context: Connect pipeline analytics to broader revenue expectations, software portfolio performance, and the drivers of the P&L.
- Insight to action: Translate complex findings into clear, actionable recommendations for business planning and decision-making for both technical and non-technical stakeholders.
- Applied ML and AI: Explore and prototype ways machine learning, LLMs, and AI agents can strengthen our analytics, such as extracting signal from opportunity notes and unstructured CRM fields or automating multi-step analytical workflows.