Data Science & Business Analytics Intern

About the role#

Our Solutions Group analyzes the financial performance of our software product portfolio and the drivers of our P&L. We are looking for an analytically strong, business-minded student to make our forecasting more predictive and robust. You will work deeply with Salesforce pipeline data to build models that segment and score opportunities, forecast revenue, and surface leading indicators behind forecast outcomes. You will also turn that work into insights that shape business planning.

What you'll do#

• Develop and enhance predictive models that improve revenue forecasting accuracy, strengthen pipeline visibility, and give forward-looking insight into revenue performance.
• Analyze Salesforce opportunity and pipeline data to find patterns in deal progression, stage conversion, win rates, deal velocity, and revenue realization.
• Apply statistical analysis, clustering, regression, classification, and other techniques to segment opportunities, estimate their likelihood and revenue potential, and identify key drivers of forecast outcomes.
• Identify leading indicators, pipeline risks, and emerging trends to explain forecast variability and improve the reliability of revenue projections.
• Explore and document Salesforce data structures, and build clean, well-modeled datasets and data pipelines to support repeatable analysis and modeling.
• Connect pipeline analytics to broader revenue expectations, software portfolio performance, and the drivers of the P&L.
• Translate complex findings into clear, actionable recommendations for business planning and decision-making for both technical and non-technical stakeholders.
• 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.
• Build scalable, repeatable analytical processes, and monitor, backtest, and refine models over time to improve forecast reliability.

What you'll need#

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

Location & details#

• This is a paid internship for Winter 2027, with program dates from January 18 through April 23, 2027.
• The role is based at our headquarters in Durham, North Carolina.
• This position follows a hybrid work modality. You must be able to work on-site at least one day per week, with two days preferred.
• Anticipated working hours are 15-30 per week.
• We are unable to provide visa sponsorship for this position.