Data Science & Business Analytics Intern

Toshiba Global Commerce Solutions - External•Durham, NC

2d•Hybrid

About The Position

The Data Science & Business Analytics Intern will support the Solutions Group in analyzing the financial performance of Toshiba Global Commerce Solutions’ software portfolio and the drivers of its P&L. The intern will work with Salesforce pipeline data to develop models that segment and score opportunities, forecast revenue, and identify leading indicators behind forecast outcomes. The role also involves translating analytical work into insights that inform business planning. The internship is based at the company’s headquarters in Durham, North Carolina (Research Triangle Park), with on-site work required at least one day per week and two days preferred. The Spring 2027 program is expected to run from January 18 through April 23, 2027. Anticipated working hours are 15–30 per week; the final schedule will be determined with the intern’s manager. Toshiba Global Commerce Solutions will not provide current or future visa sponsorship for this position.

Requirements

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 such as regression, classification, clustering, or time-series forecasting through work, internships, research, or substantial projects.
Strong understanding of AI and LLMs, including how they work, their limitations, and their potential analytics applications.
Experience working with Salesforce or other CRM data, including opportunities, stages, accounts, and pipeline history.
Proficiency in Python or R and 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 such as Power Query and Power Automate for data analysis and workflow automation.
Experience with data visualization tools such as Power BI or Tableau.
Strong business judgment, analytical thinking, and attention to detail, with the ability to clearly explain technical results to non-technical audiences.
Curiosity, initiative, willingness to experiment with new technologies, take ownership of tasks, and learn through hands-on problem-solving.
Ability to work on-site at the Durham, NC headquarters at least one day per week; two days are preferred.
Ability to work without current or future visa sponsorship.

Nice To Haves

Pursuing a master’s degree in a quantitative or analytics-focused program.
Coursework, internship, or project experience in finance, accounting, FP&A, or revenue/sales operations.
Basic understanding of P&L structure and how revenue drivers affect financial performance.
Experience with agentic AI, AI agents, and multi-step AI workflows.
Experience building AI-powered applications, automated workflows, or prototypes.

Responsibilities

Develop and enhance predictive models to improve revenue forecasting accuracy, pipeline visibility, and forward-looking insight into revenue performance.
Analyze Salesforce opportunity and pipeline data for patterns in deal progression, stage conversion, win rates, deal velocity, and revenue realization.
Use statistical analysis, clustering, regression, classification, and other techniques to segment opportunities, estimate likelihood and revenue potential, and identify forecast drivers.
Identify leading indicators, pipeline risks, and emerging trends that explain forecast variability and improve revenue projection reliability.
Explore and document Salesforce data structures, and build clean, well-modeled datasets and data pipelines for repeatable analysis and modeling.
Connect pipeline analytics to broader revenue expectations, software portfolio performance, and P&L drivers.
Translate findings into clear, actionable recommendations for business planning and decision-making for technical and non-technical stakeholders.
Explore and prototype applications of machine learning, LLMs, and AI agents for analytics, including extracting signals from opportunity notes and unstructured CRM fields or automating analytical workflows.
Build scalable, repeatable analytical processes and monitor, backtest, and refine models over time.
Work on-site at the Durham headquarters at least one day per week; two days per week are preferred.