Customer Data ML Intern

AvePoint • Arlington, VA

4h • Onsite

About The Position

We are seeking a Customer Data Machine Learning Intern to help enhance and evolve our opportunity health scoring model, a critical capability used to improve pipeline quality, win rates, and revenue outcomes. This role will focus on expanding feature engineering, improving model performance, and supporting experimentation using customer, sales, and product data within Microsoft Fabric. The intern will work closely with Data Science, Analytics, and RevOps partners to integrate additional signals and ensure outputs are actionable for sales stakeholders. This is a hands-on opportunity to apply machine learning in a real business context and contribute directly to revenue-driving insights.

Requirements

• Currently pursuing a degree in Computer Science, Data Science, Machine Learning, Statistics, Engineering, Analytics, or a related field (rising sophomore through graduate level).
• Strong interest in applied machine learning and predictive modeling.
• Experience with Python or similar languages used for data analysis and machine learning.
• Familiarity with machine learning concepts, including feature engineering, model training, and evaluation metrics.
• Exposure to SQL and structured datasets.
• Strong analytical and problem-solving skills with attention to detail and data quality.
• Ability to work with ambiguity and iterate in an experimental environment.
• Clear communication skills and ability to explain technical concepts to non-technical stakeholders.
• Curiosity, ownership mindset, and eagerness to learn applied machine learning in a business context.

Nice To Haves

• Experience or interest in Microsoft Fabric, Azure, or modern data platforms is a plus.

Responsibilities

• Review and analyze the existing opportunity health scoring model, including features, logic, and performance.
• Explore and integrate multiple data sources such as CRM, sales activity, product usage, and historical deal data.
• Design and develop new features to improve predictive accuracy (e.g., engagement trends, activity velocity, deal progression signals).
• Build and maintain feature engineering pipelines to support model development and experimentation.
• Train, test, and evaluate machine learning models and compare results against existing baselines.
• Optimize model performance through tuning and iteration using business-relevant metrics.
• Support integration of improved models into existing scoring and reporting pipelines.
• Validate outputs with Analytics, RevOps, and sales stakeholders.
• Document model logic, features, assumptions, and recommendations for future improvements.

What This Job Offers

• Career Level: Intern
• Education Level: No Education Listed
• Number of Employees: 501-1,000 employees